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324 lines
15 KiB
324 lines
15 KiB
# 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 V4 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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from nets import inception_utils
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slim = tf.contrib.slim
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def block_inception_a(inputs, scope=None, reuse=None):
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"""Builds Inception-A block for Inception v4 network."""
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# By default use stride=1 and SAME padding
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with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
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return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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def block_reduction_a(inputs, scope=None, reuse=None):
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"""Builds Reduction-A block for Inception v4 network."""
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# By default use stride=1 and SAME padding
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with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(inputs, 384, [3, 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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branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
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branch_1 = slim.conv2d(branch_1, 256, [3, 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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branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
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scope='MaxPool_1a_3x3')
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return tf.concat(3, [branch_0, branch_1, branch_2])
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def block_inception_b(inputs, scope=None, reuse=None):
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"""Builds Inception-B block for Inception v4 network."""
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# By default use stride=1 and SAME padding
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with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
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branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
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branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
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branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
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branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
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return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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def block_reduction_b(inputs, scope=None, reuse=None):
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"""Builds Reduction-B block for Inception v4 network."""
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# By default use stride=1 and SAME padding
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with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
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branch_0 = slim.conv2d(branch_0, 192, [3, 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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branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
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branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
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branch_1 = slim.conv2d(branch_1, 320, [3, 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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branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
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scope='MaxPool_1a_3x3')
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return tf.concat(3, [branch_0, branch_1, branch_2])
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def block_inception_c(inputs, scope=None, reuse=None):
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"""Builds Inception-C block for Inception v4 network."""
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# By default use stride=1 and SAME padding
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with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = tf.concat(3, [
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slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
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slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
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branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
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branch_2 = tf.concat(3, [
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slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
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slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
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return tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
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"""Creates the Inception V4 network up to the given final endpoint.
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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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final_endpoint: specifies the endpoint to construct the network up to.
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It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
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'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
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'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
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'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
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'Mixed_7d']
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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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Raises:
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ValueError: if final_endpoint is not set to one of the predefined values,
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"""
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end_points = {}
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def add_and_check_final(name, net):
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end_points[name] = net
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return name == final_endpoint
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with tf.variable_scope(scope, 'InceptionV4', [inputs]):
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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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# 299 x 299 x 3
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net = slim.conv2d(inputs, 32, [3, 3], stride=2,
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padding='VALID', scope='Conv2d_1a_3x3')
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if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
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# 149 x 149 x 32
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net = slim.conv2d(net, 32, [3, 3], padding='VALID',
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scope='Conv2d_2a_3x3')
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if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
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# 147 x 147 x 32
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net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
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if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
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# 147 x 147 x 64
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with tf.variable_scope('Mixed_3a'):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
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scope='MaxPool_0a_3x3')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
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scope='Conv2d_0a_3x3')
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net = tf.concat(3, [branch_0, branch_1])
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if add_and_check_final('Mixed_3a', net): return net, end_points
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# 73 x 73 x 160
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with tf.variable_scope('Mixed_4a'):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
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branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
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branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
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branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
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scope='Conv2d_1a_3x3')
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net = tf.concat(3, [branch_0, branch_1])
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if add_and_check_final('Mixed_4a', net): return net, end_points
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# 71 x 71 x 192
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with tf.variable_scope('Mixed_5a'):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 192, [3, 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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branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
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scope='MaxPool_1a_3x3')
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net = tf.concat(3, [branch_0, branch_1])
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if add_and_check_final('Mixed_5a', net): return net, end_points
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# 35 x 35 x 384
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# 4 x Inception-A blocks
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for idx in xrange(4):
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block_scope = 'Mixed_5' + chr(ord('b') + idx)
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net = block_inception_a(net, block_scope)
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if add_and_check_final(block_scope, net): return net, end_points
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# 35 x 35 x 384
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# Reduction-A block
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net = block_reduction_a(net, 'Mixed_6a')
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if add_and_check_final('Mixed_6a', net): return net, end_points
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# 17 x 17 x 1024
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# 7 x Inception-B blocks
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for idx in xrange(7):
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block_scope = 'Mixed_6' + chr(ord('b') + idx)
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net = block_inception_b(net, block_scope)
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if add_and_check_final(block_scope, net): return net, end_points
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# 17 x 17 x 1024
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# Reduction-B block
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net = block_reduction_b(net, 'Mixed_7a')
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if add_and_check_final('Mixed_7a', net): return net, end_points
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# 8 x 8 x 1536
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# 3 x Inception-C blocks
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for idx in xrange(3):
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block_scope = 'Mixed_7' + chr(ord('b') + idx)
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net = block_inception_c(net, block_scope)
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if add_and_check_final(block_scope, net): return net, end_points
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raise ValueError('Unknown final endpoint %s' % final_endpoint)
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def inception_v4(inputs, num_classes=1001, is_training=True,
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dropout_keep_prob=0.8,
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reuse=None,
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scope='InceptionV4',
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create_aux_logits=True):
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"""Creates the Inception V4 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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create_aux_logits: Whether to include the auxilliary logits.
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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, 'InceptionV4', [inputs], reuse=reuse) as scope:
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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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net, end_points = inception_v4_base(inputs, scope=scope)
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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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# Auxiliary Head logits
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if create_aux_logits:
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with tf.variable_scope('AuxLogits'):
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# 17 x 17 x 1024
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aux_logits = end_points['Mixed_6h']
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aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
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padding='VALID',
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scope='AvgPool_1a_5x5')
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aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
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scope='Conv2d_1b_1x1')
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aux_logits = slim.conv2d(aux_logits, 768,
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aux_logits.get_shape()[1:3],
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padding='VALID', scope='Conv2d_2a')
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aux_logits = slim.flatten(aux_logits)
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aux_logits = slim.fully_connected(aux_logits, num_classes,
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activation_fn=None,
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scope='Aux_logits')
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end_points['AuxLogits'] = aux_logits
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# Final pooling and prediction
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with tf.variable_scope('Logits'):
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# 8 x 8 x 1536
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net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
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scope='AvgPool_1a')
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# 1 x 1 x 1536
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net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
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net = slim.flatten(net, scope='PreLogitsFlatten')
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end_points['PreLogitsFlatten'] = net
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# 1536
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logits = slim.fully_connected(net, num_classes, activation_fn=None,
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scope='Logits')
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end_points['Logits'] = logits
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end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
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return logits, end_points
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inception_v4.default_image_size = 299
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inception_v4_arg_scope = inception_utils.inception_arg_scope
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