Added InceptionResnetV1

bob/learn/tensorflow/network/InceptionResnetV1.py

+# 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 Resnet V1 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
+import tensorflow.contrib.slim as slim
+
+# Inception-Renset-A
+def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+    """Builds the 35x35 resnet block."""
+    with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+            tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
+            tower_conv2_1 = slim.conv2d(tower_conv2_0, 32, 3, scope='Conv2d_0b_3x3')
+            tower_conv2_2 = slim.conv2d(tower_conv2_1, 32, 3, scope='Conv2d_0c_3x3')
+        mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                         activation_fn=None, scope='Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+
+# Inception-Renset-B
+def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+    """Builds the 17x17 resnet block."""
+    with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 128, 1, scope='Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 128, [1, 7],
+                                        scope='Conv2d_0b_1x7')
+            tower_conv1_2 = slim.conv2d(tower_conv1_1, 128, [7, 1],
+                                        scope='Conv2d_0c_7x1')
+        mixed = tf.concat([tower_conv, tower_conv1_2], 3)
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                         activation_fn=None, scope='Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+
+
+# Inception-Resnet-C
+def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
+    """Builds the 8x8 resnet block."""
+    with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 192, [1, 3],
+                                        scope='Conv2d_0b_1x3')
+            tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [3, 1],
+                                        scope='Conv2d_0c_3x1')
+        mixed = tf.concat([tower_conv, tower_conv1_2], 3)
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
+                         activation_fn=None, scope='Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+  
+def reduction_a(net, k, l, m, n):
+    with tf.variable_scope('Branch_0'):
+        tower_conv = slim.conv2d(net, n, 3, stride=2, padding='VALID',
+                                 scope='Conv2d_1a_3x3')
+    with tf.variable_scope('Branch_1'):
+        tower_conv1_0 = slim.conv2d(net, k, 1, scope='Conv2d_0a_1x1')
+        tower_conv1_1 = slim.conv2d(tower_conv1_0, l, 3,
+                                    scope='Conv2d_0b_3x3')
+        tower_conv1_2 = slim.conv2d(tower_conv1_1, m, 3,
+                                    stride=2, padding='VALID',
+                                    scope='Conv2d_1a_3x3')
+    with tf.variable_scope('Branch_2'):
+        tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
+                                     scope='MaxPool_1a_3x3')
+    net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
+    return net
+
+def reduction_b(net):
+    with tf.variable_scope('Branch_0'):
+        tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+        tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
+                                   padding='VALID', scope='Conv2d_1a_3x3')
+    with tf.variable_scope('Branch_1'):
+        tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+        tower_conv1_1 = slim.conv2d(tower_conv1, 256, 3, stride=2,
+                                    padding='VALID', scope='Conv2d_1a_3x3')
+    with tf.variable_scope('Branch_2'):
+        tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+        tower_conv2_1 = slim.conv2d(tower_conv2, 256, 3,
+                                    scope='Conv2d_0b_3x3')
+        tower_conv2_2 = slim.conv2d(tower_conv2_1, 256, 3, stride=2,
+                                    padding='VALID', scope='Conv2d_1a_3x3')
+    with tf.variable_scope('Branch_3'):
+        tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
+                                     scope='MaxPool_1a_3x3')
+    net = tf.concat([tower_conv_1, tower_conv1_1,
+                        tower_conv2_2, tower_pool], 3)
+    return net
+  
+def inference(images, keep_probability, phase_train=True, 
+              bottleneck_layer_size=128, weight_decay=0.0, reuse=None):
+    batch_norm_params = {
+        # Decay for the moving averages.
+        'decay': 0.995,
+        # epsilon to prevent 0s in variance.
+        'epsilon': 0.001,
+        # force in-place updates of mean and variance estimates
+        'updates_collections': None,
+        # Moving averages ends up in the trainable variables collection
+        'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
+    }
+    
+    with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                        weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
+                        weights_regularizer=slim.l2_regularizer(weight_decay),
+                        normalizer_fn=slim.batch_norm,
+                        normalizer_params=batch_norm_params):
+        return inception_resnet_v1(images, is_training=phase_train,
+              dropout_keep_prob=keep_probability, bottleneck_layer_size=bottleneck_layer_size, reuse=reuse)
+
+
+def inception_resnet_v1(inputs, is_training=True,
+                        dropout_keep_prob=0.8,
+                        bottleneck_layer_size=128,
+                        reuse=None, 
+                        scope='InceptionResnetV1'):
+    """Creates the Inception Resnet V1 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.
+    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, 'InceptionResnetV1', [inputs], reuse=reuse):
+        with slim.arg_scope([slim.batch_norm, slim.dropout],
+                            is_training=is_training):
+            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                                stride=1, padding='SAME'):
+      
+                # 149 x 149 x 32
+                net = slim.conv2d(inputs, 32, 3, stride=2, padding='VALID',
+                                  scope='Conv2d_1a_3x3')
+                end_points['Conv2d_1a_3x3'] = net
+                # 147 x 147 x 32
+                net = slim.conv2d(net, 32, 3, padding='VALID',
+                                  scope='Conv2d_2a_3x3')
+                end_points['Conv2d_2a_3x3'] = net
+                # 147 x 147 x 64
+                net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
+                end_points['Conv2d_2b_3x3'] = net
+                # 73 x 73 x 64
+                net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
+                                      scope='MaxPool_3a_3x3')
+                end_points['MaxPool_3a_3x3'] = net
+                # 73 x 73 x 80
+                net = slim.conv2d(net, 80, 1, padding='VALID',
+                                  scope='Conv2d_3b_1x1')
+                end_points['Conv2d_3b_1x1'] = net
+                # 71 x 71 x 192
+                net = slim.conv2d(net, 192, 3, padding='VALID',
+                                  scope='Conv2d_4a_3x3')
+                end_points['Conv2d_4a_3x3'] = net
+                # 35 x 35 x 256
+                net = slim.conv2d(net, 256, 3, stride=2, padding='VALID',
+                                  scope='Conv2d_4b_3x3')
+                end_points['Conv2d_4b_3x3'] = net
+                
+                # 5 x Inception-resnet-A
+                net = slim.repeat(net, 5, block35, scale=0.17)
+                end_points['Mixed_5a'] = net
+        
+                # Reduction-A
+                with tf.variable_scope('Mixed_6a'):
+                    net = reduction_a(net, 192, 192, 256, 384)
+                end_points['Mixed_6a'] = net
+                
+                # 10 x Inception-Resnet-B
+                net = slim.repeat(net, 10, block17, scale=0.10)
+                end_points['Mixed_6b'] = net
+                
+                # Reduction-B
+                with tf.variable_scope('Mixed_7a'):
+                    net = reduction_b(net)
+                end_points['Mixed_7a'] = net
+                
+                # 5 x Inception-Resnet-C
+                net = slim.repeat(net, 5, block8, scale=0.20)
+                end_points['Mixed_8a'] = net
+                
+                net = block8(net, activation_fn=None)
+                end_points['Mixed_8b'] = net
+                
+                with tf.variable_scope('Logits'):
+                    end_points['PrePool'] = net
+                    #pylint: disable=no-member
+                    net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
+                                          scope='AvgPool_1a_8x8')
+                    net = slim.flatten(net)
+          
+                    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                                       scope='Dropout')
+          
+                    end_points['PreLogitsFlatten'] = net
+                
+                net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None, 
+                        scope='Bottleneck', reuse=False)
+  
+    return net, end_points

bob/learn/tensorflow/network/__init__.py

@@ -5,6 +5,7 @@ from .Dummy import Dummy
 from .MLP import MLP
 from .Embedding import Embedding
 from .InceptionResnetV2 import inception_resnet_v2
+from .InceptionResnetV1 import inception_resnet_v1
 
 
 # gets sphinx autodoc done right - don't remove it