--- /dev/null
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition of the Inception V4 architecture.
+
+As described in http://arxiv.org/abs/1602.07261.
+
+ Inception-v4, Inception-ResNet and the Impact of Residual Connections
+ on Learning
+ Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+
+
+def block_inception_a(inputs, scope=None, reuse=None):
+ """Builds Inception-A block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
+ branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def block_reduction_a(inputs, scope=None, reuse=None):
+ """Builds Reduction-A block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
+ branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_1a_3x3')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_b(inputs, scope=None, reuse=None):
+ """Builds Inception-B block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def block_reduction_b(inputs, scope=None, reuse=None):
+ """Builds Reduction-B block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
+ branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
+ branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_1a_3x3')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_c(inputs, scope=None, reuse=None):
+ """Builds Inception-C block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
+ slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
+ branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
+ branch_2 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
+ slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+
+
+def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
+ """Creates the Inception V4 network up to the given final endpoint.
+
+ Args:
+ inputs: a 4-D tensor of size [batch_size, height, width, 3].
+ final_endpoint: specifies the endpoint to construct the network up to.
+ It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+ 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+ 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
+ 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
+ 'Mixed_7d']
+ scope: Optional variable_scope.
+
+ Returns:
+ logits: the logits outputs of the model.
+ end_points: the set of end_points from the inception model.
+
+ Raises:
+ ValueError: if final_endpoint is not set to one of the predefined values,
+ """
+ end_points = {}
+
+ def add_and_check_final(name, net):
+ end_points[name] = net
+ return name == final_endpoint
+
+ with tf.variable_scope(scope, 'InceptionV4', [inputs]):
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ # 299 x 299 x 3
+ net = slim.conv2d(inputs, 32, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
+ # 149 x 149 x 32
+ net = slim.conv2d(net, 32, [3, 3], padding='VALID',
+ scope='Conv2d_2a_3x3')
+ if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
+ # 147 x 147 x 32
+ net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
+ if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
+ # 147 x 147 x 64
+ with tf.variable_scope('Mixed_3a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_0a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
+ scope='Conv2d_0a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_3a', net): return net, end_points
+
+ # 73 x 73 x 160
+ with tf.variable_scope('Mixed_4a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+ branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
+ branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
+ scope='Conv2d_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_4a', net): return net, end_points
+
+ # 71 x 71 x 192
+ with tf.variable_scope('Mixed_5a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_5a', net): return net, end_points
+
+ # 35 x 35 x 384
+ # 4 x Inception-A blocks
+ for idx in range(4):
+ block_scope = 'Mixed_5' + chr(ord('b') + idx)
+ net = block_inception_a(net, block_scope)
+ if add_and_check_final(block_scope, net): return net, end_points
+
+ # 35 x 35 x 384
+ # Reduction-A block
+ net = block_reduction_a(net, 'Mixed_6a')
+ if add_and_check_final('Mixed_6a', net): return net, end_points
+
+ # 17 x 17 x 1024
+ # 7 x Inception-B blocks
+ for idx in range(7):
+ block_scope = 'Mixed_6' + chr(ord('b') + idx)
+ net = block_inception_b(net, block_scope)
+ if add_and_check_final(block_scope, net): return net, end_points
+
+ # 17 x 17 x 1024
+ # Reduction-B block
+ net = block_reduction_b(net, 'Mixed_7a')
+ if add_and_check_final('Mixed_7a', net): return net, end_points
+
+ # 8 x 8 x 1536
+ # 3 x Inception-C blocks
+ for idx in range(3):
+ block_scope = 'Mixed_7' + chr(ord('b') + idx)
+ net = block_inception_c(net, block_scope)
+ if add_and_check_final(block_scope, net): return net, end_points
+ raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v4(inputs, num_classes=1001, is_training=True,
+ dropout_keep_prob=0.8,
+ reuse=None,
+ scope='InceptionV4',
+ create_aux_logits=True):
+ """Creates the Inception V4 model.
+
+ Args:
+ inputs: a 4-D tensor of size [batch_size, height, width, 3].
+ num_classes: number of predicted classes.
+ is_training: whether is training or not.
+ dropout_keep_prob: float, the fraction to keep before final layer.
+ reuse: whether or not the network and its variables should be reused. To be
+ able to reuse 'scope' must be given.
+ scope: Optional variable_scope.
+ create_aux_logits: Whether to include the auxiliary logits.
+
+ Returns:
+ logits: the logits outputs of the model.
+ end_points: the set of end_points from the inception model.
+ """
+ end_points = {}
+ with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope:
+ with slim.arg_scope([slim.batch_norm, slim.dropout],
+ is_training=is_training):
+ net, end_points = inception_v4_base(inputs, scope=scope)
+
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ # Auxiliary Head logits
+ if create_aux_logits:
+ with tf.variable_scope('AuxLogits'):
+ # 17 x 17 x 1024
+ aux_logits = end_points['Mixed_6h']
+ aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
+ padding='VALID',
+ scope='AvgPool_1a_5x5')
+ aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
+ scope='Conv2d_1b_1x1')
+ aux_logits = slim.conv2d(aux_logits, 768,
+ aux_logits.get_shape()[1:3],
+ padding='VALID', scope='Conv2d_2a')
+ aux_logits = slim.flatten(aux_logits)
+ aux_logits = slim.fully_connected(aux_logits, num_classes,
+ activation_fn=None,
+ scope='Aux_logits')
+ end_points['AuxLogits'] = aux_logits
+
+ # Final pooling and prediction
+ with tf.variable_scope('Logits'):
+ # 8 x 8 x 1536
+ net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
+ scope='AvgPool_1a')
+ # 1 x 1 x 1536
+ net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
+ net = slim.flatten(net, scope='PreLogitsFlatten')
+ end_points['PreLogitsFlatten'] = net
+ # 1536
+ logits = slim.fully_connected(net, num_classes, activation_fn=None,
+ scope='Logits')
+ end_points['Logits'] = logits
+ end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
+ return logits, end_points
+inception_v4.default_image_size = 299
+
+
+inception_v4_arg_scope = inception_utils.inception_arg_scope
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