--- /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 Resnet V2 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
+
+slim = tf.contrib.slim
+
+
+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, 48, 3, scope='Conv2d_0b_3x3')
+ tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
+ mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
+ 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 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, 192, 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, 160, [1, 7],
+ scope='Conv2d_0b_1x7')
+ tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
+ scope='Conv2d_0c_7x1')
+ mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
+ 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 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, 224, [1, 3],
+ scope='Conv2d_0b_1x3')
+ tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
+ scope='Conv2d_0c_3x1')
+ mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
+ 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 inception_resnet_v2_base(inputs,
+ final_endpoint='Conv2d_7b_1x1',
+ output_stride=16,
+ align_feature_maps=False,
+ scope=None):
+ """Inception model from http://arxiv.org/abs/1602.07261.
+
+ Constructs an Inception Resnet v2 network from inputs to the given final
+ endpoint. This method can construct the network up to the final inception
+ block Conv2d_7b_1x1.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ 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',
+ 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
+ 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
+ output_stride: A scalar that specifies the requested ratio of input to
+ output spatial resolution. Only supports 8 and 16.
+ align_feature_maps: When true, changes all the VALID paddings in the network
+ to SAME padding so that the feature maps are aligned.
+ scope: Optional variable_scope.
+
+ Returns:
+ tensor_out: output tensor corresponding to the final_endpoint.
+ end_points: a set of activations for external use, for example summaries or
+ losses.
+
+ Raises:
+ ValueError: if final_endpoint is not set to one of the predefined values,
+ or if the output_stride is not 8 or 16, or if the output_stride is 8 and
+ we request an end point after 'PreAuxLogits'.
+ """
+ if output_stride != 8 and output_stride != 16:
+ raise ValueError('output_stride must be 8 or 16.')
+
+ padding = 'SAME' if align_feature_maps else 'VALID'
+
+ end_points = {}
+
+ def add_and_check_final(name, net):
+ end_points[name] = net
+ return name == final_endpoint
+
+ with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
+ 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=padding,
+ scope='Conv2d_1a_3x3')
+ if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
+
+ # 147 x 147 x 32
+ net = slim.conv2d(net, 32, 3, padding=padding,
+ scope='Conv2d_2a_3x3')
+ if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
+ # 147 x 147 x 64
+ net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
+ if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
+ # 73 x 73 x 64
+ net = slim.max_pool2d(net, 3, stride=2, padding=padding,
+ scope='MaxPool_3a_3x3')
+ if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
+ # 73 x 73 x 80
+ net = slim.conv2d(net, 80, 1, padding=padding,
+ scope='Conv2d_3b_1x1')
+ if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
+ # 71 x 71 x 192
+ net = slim.conv2d(net, 192, 3, padding=padding,
+ scope='Conv2d_4a_3x3')
+ if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
+ # 35 x 35 x 192
+ net = slim.max_pool2d(net, 3, stride=2, padding=padding,
+ scope='MaxPool_5a_3x3')
+ if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points
+
+ # 35 x 35 x 320
+ with tf.variable_scope('Mixed_5b'):
+ with tf.variable_scope('Branch_0'):
+ tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
+ with tf.variable_scope('Branch_1'):
+ tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
+ tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
+ scope='Conv2d_0b_5x5')
+ with tf.variable_scope('Branch_2'):
+ tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
+ tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
+ scope='Conv2d_0b_3x3')
+ tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
+ scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
+ scope='AvgPool_0a_3x3')
+ tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(
+ [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
+
+ if add_and_check_final('Mixed_5b', net): return net, end_points
+ # TODO(alemi): Register intermediate endpoints
+ net = slim.repeat(net, 10, block35, scale=0.17)
+
+ # 17 x 17 x 1088 if output_stride == 8,
+ # 33 x 33 x 1088 if output_stride == 16
+ use_atrous = output_stride == 8
+
+ with tf.variable_scope('Mixed_6a'):
+ with tf.variable_scope('Branch_0'):
+ tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
+ padding=padding,
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
+ tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
+ scope='Conv2d_0b_3x3')
+ tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
+ stride=1 if use_atrous else 2,
+ padding=padding,
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
+ padding=padding,
+ scope='MaxPool_1a_3x3')
+ net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
+
+ if add_and_check_final('Mixed_6a', net): return net, end_points
+
+ # TODO(alemi): register intermediate endpoints
+ with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
+ net = slim.repeat(net, 20, block17, scale=0.10)
+ if add_and_check_final('PreAuxLogits', net): return net, end_points
+
+ if output_stride == 8:
+ # TODO(gpapan): Properly support output_stride for the rest of the net.
+ raise ValueError('output_stride==8 is only supported up to the '
+ 'PreAuxlogits end_point for now.')
+
+ # 8 x 8 x 2080
+ with tf.variable_scope('Mixed_7a'):
+ 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=padding,
+ 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, 288, 3, stride=2,
+ padding=padding,
+ 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, 288, 3,
+ scope='Conv2d_0b_3x3')
+ tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
+ padding=padding,
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_3'):
+ tower_pool = slim.max_pool2d(net, 3, stride=2,
+ padding=padding,
+ scope='MaxPool_1a_3x3')
+ net = tf.concat(
+ [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
+
+ if add_and_check_final('Mixed_7a', net): return net, end_points
+
+ # TODO(alemi): register intermediate endpoints
+ net = slim.repeat(net, 9, block8, scale=0.20)
+ net = block8(net, activation_fn=None)
+
+ # 8 x 8 x 1536
+ net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
+ if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points
+
+ raise ValueError('final_endpoint (%s) not recognized', final_endpoint)
+
+
+def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
+ dropout_keep_prob=0.8,#0.8
+ reuse=None,
+ scope='InceptionResnetV2',
+ create_aux_logits=True):
+ """Creates the Inception Resnet V2 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 auxilliary 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, 'InceptionResnetV2', [inputs, num_classes],
+ reuse=reuse) as scope:
+ with slim.arg_scope([slim.batch_norm, slim.dropout],
+ is_training=is_training):
+
+ net, end_points = inception_resnet_v2_base(inputs, scope=scope)
+
+ if create_aux_logits:
+ with tf.variable_scope('AuxLogits'):
+ aux = end_points['PreAuxLogits']
+ aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
+ scope='Conv2d_1a_3x3')
+ aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
+ aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
+ padding='VALID', scope='Conv2d_2a_5x5')
+ aux = slim.flatten(aux)
+ aux = slim.fully_connected(aux, num_classes, activation_fn=None,
+ scope='Logits')
+ end_points['AuxLogits'] = aux
+
+ with tf.variable_scope('Logits'):
+ 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
+ # end_points['yjr_feature'] = tf.squeeze(net, axis=0)
+
+ 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_resnet_v2.default_image_size = 299
+
+
+def inception_resnet_v2_arg_scope(weight_decay=0.00004,
+ batch_norm_decay=0.9997,
+ batch_norm_epsilon=0.001):
+ """Yields the scope with the default parameters for inception_resnet_v2.
+
+ Args:
+ weight_decay: the weight decay for weights variables.
+ batch_norm_decay: decay for the moving average of batch_norm momentums.
+ batch_norm_epsilon: small float added to variance to avoid dividing by zero.
+
+ Returns:
+ a arg_scope with the parameters needed for inception_resnet_v2.
+ """
+ # Set weight_decay for weights in conv2d and fully_connected layers.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ weights_regularizer=slim.l2_regularizer(weight_decay),
+ biases_regularizer=slim.l2_regularizer(weight_decay)):
+
+ batch_norm_params = {
+ 'decay': batch_norm_decay,
+ 'epsilon': batch_norm_epsilon,
+ }
+ # Set activation_fn and parameters for batch_norm.
+ with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
+ normalizer_fn=slim.batch_norm,
+ normalizer_params=batch_norm_params) as scope:
+ return scope
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