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281 lines
12 KiB
281 lines
12 KiB
5 years ago
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# 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 V2 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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slim = tf.contrib.slim
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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, 48, 3, scope='Conv2d_0b_3x3')
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tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
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mixed = tf.concat(3, [tower_conv, tower_conv1_1, tower_conv2_2])
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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 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, 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, 128, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
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scope='Conv2d_0b_1x7')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
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scope='Conv2d_0c_7x1')
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mixed = tf.concat(3, [tower_conv, tower_conv1_2])
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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 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, 224, [1, 3],
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scope='Conv2d_0b_1x3')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
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scope='Conv2d_0c_3x1')
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mixed = tf.concat(3, [tower_conv, tower_conv1_2])
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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 inception_resnet_v2(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='InceptionResnetV2'):
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"""Creates the Inception Resnet V2 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, 'InceptionResnetV2', [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 192
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net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
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scope='MaxPool_5a_3x3')
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end_points['MaxPool_5a_3x3'] = net
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# 35 x 35 x 320
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with tf.variable_scope('Mixed_5b'):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
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with tf.variable_scope('Branch_1'):
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tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
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scope='Conv2d_0b_5x5')
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with tf.variable_scope('Branch_2'):
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tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
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tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
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scope='Conv2d_0b_3x3')
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tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
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scope='AvgPool_0a_3x3')
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tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
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scope='Conv2d_0b_1x1')
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net = tf.concat(3, [tower_conv, tower_conv1_1,
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tower_conv2_2, tower_pool_1])
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end_points['Mixed_5b'] = net
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net = slim.repeat(net, 10, block35, scale=0.17)
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# 17 x 17 x 1024
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with tf.variable_scope('Mixed_6a'):
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with tf.variable_scope('Branch_0'):
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tower_conv = slim.conv2d(net, 384, 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, 256, 1, scope='Conv2d_0a_1x1')
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tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
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scope='Conv2d_0b_3x3')
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tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 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(3, [tower_conv, tower_conv1_2, tower_pool])
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end_points['Mixed_6a'] = net
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net = slim.repeat(net, 20, block17, scale=0.10)
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# Auxillary tower
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with tf.variable_scope('AuxLogits'):
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aux = slim.avg_pool2d(net, 5, stride=3, padding='VALID',
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scope='Conv2d_1a_3x3')
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aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
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aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
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padding='VALID', scope='Conv2d_2a_5x5')
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aux = slim.flatten(aux)
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aux = slim.fully_connected(aux, num_classes, activation_fn=None,
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scope='Logits')
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end_points['AuxLogits'] = aux
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with tf.variable_scope('Mixed_7a'):
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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, 288, 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, 288, 3,
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scope='Conv2d_0b_3x3')
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tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 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(3, [tower_conv_1, tower_conv1_1,
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tower_conv2_2, tower_pool])
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end_points['Mixed_7a'] = net
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net = slim.repeat(net, 9, block8, scale=0.20)
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net = block8(net, activation_fn=None)
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net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
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end_points['Conv2d_7b_1x1'] = net
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with tf.variable_scope('Logits'):
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end_points['PrePool'] = net
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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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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_resnet_v2.default_image_size = 299
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def inception_resnet_v2_arg_scope(weight_decay=0.00004,
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batch_norm_decay=0.9997,
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batch_norm_epsilon=0.001):
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"""Yields the scope with the default parameters for inception_resnet_v2.
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Args:
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weight_decay: the weight decay for weights variables.
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batch_norm_decay: decay for the moving average of batch_norm momentums.
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batch_norm_epsilon: small float added to variance to avoid dividing by zero.
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Returns:
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a arg_scope with the parameters needed for inception_resnet_v2.
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"""
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# Set weight_decay for weights in conv2d and fully_connected layers.
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with slim.arg_scope([slim.conv2d, slim.fully_connected],
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weights_regularizer=slim.l2_regularizer(weight_decay),
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biases_regularizer=slim.l2_regularizer(weight_decay)):
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batch_norm_params = {
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'decay': batch_norm_decay,
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'epsilon': batch_norm_epsilon,
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}
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# Set activation_fn and parameters for batch_norm.
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with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
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normalizer_fn=slim.batch_norm,
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normalizer_params=batch_norm_params) as scope:
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return scope
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