160 lines
8.8 KiB
Python
160 lines
8.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import tensorflow.contrib.slim as slim
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def inference(images, keep_probability, phase_train=True,
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class_num=1000, weight_decay=0.0, reuse=None):
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batch_norm_params = {
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'decay': 0.995,
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'epsilon': 0.001,
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'updates_collections': None,
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'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
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}
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with slim.arg_scope([slim.conv2d, slim.separable_conv2d,slim.fully_connected],
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weights_initializer=slim.initializers.xavier_initializer(),
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weights_regularizer=slim.l2_regularizer(weight_decay),
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normalizer_fn=slim.batch_norm,
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normalizer_params=batch_norm_params):
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return mobilenet_v1(images, is_training=phase_train,
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dropout_keep_prob=keep_probability, class_num=class_num, reuse=reuse)
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def block_14x14(net, outputs, scope=None, reuse=None):
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with tf.variable_scope(scope, "block-14x14", reuse=reuse):
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=1,
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padding='SAME',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d')
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net = slim.conv2d(net, outputs, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,scope='conv')
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return net
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def mobilenet_v1(inputs, is_training=True,
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dropout_keep_prob=0.5,
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class_num=1000,
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reuse=None,
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scope='MobileNetV1'):
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end_points = {}
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net = None
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_l = 0
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with tf.variable_scope(scope, 'MobileNetV1', [inputs], reuse=reuse):
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with slim.arg_scope([slim.batch_norm, slim.dropout],
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is_training=is_training):
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with slim.arg_scope([slim.conv2d,slim.separable_conv2d],
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stride=1, padding='SAME',normalizer_fn=slim.batch_norm):
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inputs = tf.space_to_batch(inputs,[[1,1],[1,1]],block_size=1,name=None)
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# ------------------------x224------------------------- #
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net = slim.conv2d(inputs, 32, 3, stride=2,padding='VALID',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_3x3'%(_l))
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_l += 1
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# ------------------------x112------------------------- #
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=1,
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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net = slim.conv2d(net, 64, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=2,
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padding='VALID',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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# ------------------------x56-------------------------- #
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net = slim.conv2d(net, 128, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=1,
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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net = slim.conv2d(net, 128, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=2,
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padding='VALID',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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# ------------------------x28-------------------------- #
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net = slim.conv2d(net, 256, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=1,
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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net = slim.conv2d(net, 256, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=2,
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padding='VALID',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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# ------------------------x14-------------------------- #
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net = slim.conv2d(net, 512, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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with tf.variable_scope(scope,'block_repeat_%i'%(_l)):
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for _k in range(5):
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net = block_14x14(net,512)
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_l += 1
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net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=2,
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padding='VALID',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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# -------------------------x7-------------------------- #
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net = slim.conv2d(net, 1024, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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net = slim.separable_conv2d(net, None, [3, 3],
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depth_multiplier=1,
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stride=1,
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padding='SAME',
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normalizer_fn=slim.batch_norm,
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scope='dw_Conv2d_%i_3x3'%(_l))
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_l += 1
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net = slim.conv2d(net, 1024, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
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scope='Conv2d_%i_1x1'%(_l))
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_l += 1
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# ---------------------softmax out---------------------- #
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net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
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scope='AvgPool_%i'%(_l))
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_l += 1
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net = slim.flatten(net)
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='Dropout_1')
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net = slim.fully_connected(net, class_num, activation_fn=None,
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scope='Bottleneck2', reuse=False)
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return net, None
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