248 строки
12 KiB
Python
248 строки
12 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the definition of the Inception Resnet V1 architecture.
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As described in http://arxiv.org/abs/1602.07261.
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Inception-v4, Inception-ResNet and the Impact of Residual Connections
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on Learning
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Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
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"""
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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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# Inception-Resnet-A
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def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
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"""Builds the 35x35 resnet block."""
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with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
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with tf.variable_scope('Branch_1'):
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tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
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tower_conv2_1 = slim.conv2d(tower_conv2_0, 32, 3, scope='Conv2d_0b_3x3')
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tower_conv2_2 = slim.conv2d(tower_conv2_1, 32, 3, scope='Conv2d_0c_3x3')
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mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
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up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
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activation_fn=None, scope='Conv2d_1x1')
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net += scale * up
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if activation_fn:
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net = activation_fn(net)
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return net
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# Inception-Resnet-B
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def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
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"""Builds the 17x17 resnet block."""
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with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 128, 1, scope='Conv2d_1x1')
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with tf.variable_scope('Branch_1'):
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tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 128, [1, 7],
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scope='Conv2d_0b_1x7')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, 128, [7, 1],
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scope='Conv2d_0c_7x1')
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mixed = tf.concat([tower_conv, tower_conv1_2], 3)
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up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
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activation_fn=None, scope='Conv2d_1x1')
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net += scale * up
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if activation_fn:
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net = activation_fn(net)
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return net
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# Inception-Resnet-C
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def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
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"""Builds the 8x8 resnet block."""
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with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
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with tf.variable_scope('Branch_1'):
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tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 192, [1, 3],
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scope='Conv2d_0b_1x3')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [3, 1],
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scope='Conv2d_0c_3x1')
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mixed = tf.concat([tower_conv, tower_conv1_2], 3)
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up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
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activation_fn=None, scope='Conv2d_1x1')
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net += scale * up
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if activation_fn:
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net = activation_fn(net)
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return net
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def reduction_a(net, k, l, m, n):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, n, 3, stride=2, padding='VALID',
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_1'):
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tower_conv1_0 = slim.conv2d(net, k, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, l, 3,
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scope='Conv2d_0b_3x3')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, m, 3,
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stride=2, padding='VALID',
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_2'):
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tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
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scope='MaxPool_1a_3x3')
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net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
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return net
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def reduction_b(net):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
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tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
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padding='VALID', scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_1'):
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tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1, 256, 3, stride=2,
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padding='VALID', scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_2'):
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tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
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tower_conv2_1 = slim.conv2d(tower_conv2, 256, 3,
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scope='Conv2d_0b_3x3')
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tower_conv2_2 = slim.conv2d(tower_conv2_1, 256, 3, stride=2,
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padding='VALID', scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_3'):
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tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
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scope='MaxPool_1a_3x3')
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net = tf.concat([tower_conv_1, tower_conv1_1,
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tower_conv2_2, tower_pool], 3)
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return net
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def inference(images, keep_probability, phase_train=True,
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bottleneck_layer_size=128, weight_decay=0.0, reuse=None):
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batch_norm_params = {
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# Decay for the moving averages.
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'decay': 0.995,
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# epsilon to prevent 0s in variance.
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'epsilon': 0.001,
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# force in-place updates of mean and variance estimates
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'updates_collections': None,
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# Moving averages ends up in the trainable variables collection
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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.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 inception_resnet_v1(images, is_training=phase_train,
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dropout_keep_prob=keep_probability, bottleneck_layer_size=bottleneck_layer_size, reuse=reuse)
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def inception_resnet_v1(inputs, is_training=True,
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dropout_keep_prob=0.8,
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bottleneck_layer_size=128,
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reuse=None,
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scope='InceptionResnetV1'):
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"""Creates the Inception Resnet V1 model.
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Args:
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inputs: a 4-D tensor of size [batch_size, height, width, 3].
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num_classes: number of predicted classes.
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is_training: whether is training or not.
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dropout_keep_prob: float, the fraction to keep before final layer.
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reuse: whether or not the network and its variables should be reused. To be
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able to reuse 'scope' must be given.
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scope: Optional variable_scope.
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Returns:
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logits: the logits outputs of the model.
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end_points: the set of end_points from the inception model.
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"""
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end_points = {}
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with tf.variable_scope(scope, 'InceptionResnetV1', [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.max_pool2d, slim.avg_pool2d],
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stride=1, padding='SAME'):
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# 149 x 149 x 32
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net = slim.conv2d(inputs, 32, 3, stride=2, padding='VALID',
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scope='Conv2d_1a_3x3')
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end_points['Conv2d_1a_3x3'] = net
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# 147 x 147 x 32
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net = slim.conv2d(net, 32, 3, padding='VALID',
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scope='Conv2d_2a_3x3')
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end_points['Conv2d_2a_3x3'] = net
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# 147 x 147 x 64
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net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
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end_points['Conv2d_2b_3x3'] = net
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# 73 x 73 x 64
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net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
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scope='MaxPool_3a_3x3')
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end_points['MaxPool_3a_3x3'] = net
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# 73 x 73 x 80
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net = slim.conv2d(net, 80, 1, padding='VALID',
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scope='Conv2d_3b_1x1')
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end_points['Conv2d_3b_1x1'] = net
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# 71 x 71 x 192
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net = slim.conv2d(net, 192, 3, padding='VALID',
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scope='Conv2d_4a_3x3')
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end_points['Conv2d_4a_3x3'] = net
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# 35 x 35 x 256
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net = slim.conv2d(net, 256, 3, stride=2, padding='VALID',
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scope='Conv2d_4b_3x3')
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end_points['Conv2d_4b_3x3'] = net
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# 5 x Inception-resnet-A
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net = slim.repeat(net, 5, block35, scale=0.17)
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end_points['Mixed_5a'] = net
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# Reduction-A
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with tf.variable_scope('Mixed_6a'):
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net = reduction_a(net, 192, 192, 256, 384)
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end_points['Mixed_6a'] = net
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# 10 x Inception-Resnet-B
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net = slim.repeat(net, 10, block17, scale=0.10)
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end_points['Mixed_6b'] = net
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# Reduction-B
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with tf.variable_scope('Mixed_7a'):
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net = reduction_b(net)
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end_points['Mixed_7a'] = net
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# 5 x Inception-Resnet-C
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net = slim.repeat(net, 5, block8, scale=0.20)
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end_points['Mixed_8a'] = net
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net = block8(net, activation_fn=None)
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end_points['Mixed_8b'] = net
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with tf.variable_scope('Logits'):
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end_points['PrePool'] = net
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#pylint: disable=no-member
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net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
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scope='AvgPool_1a_8x8')
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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')
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end_points['PreLogitsFlatten'] = net
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net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None,
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scope='Bottleneck', reuse=False)
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return net, end_points
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