--- /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 for inception v1 classification network."""
+
+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
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def inception_v1_base(inputs,
+ final_endpoint='Mixed_5c',
+ scope='InceptionV1'):
+ """Defines the Inception V1 base architecture.
+
+ This architecture is defined in:
+ Going deeper with convolutions
+ Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+ Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
+ http://arxiv.org/pdf/1409.4842v1.pdf.
+
+ 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_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
+ 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
+ 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
+ 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
+ scope: Optional variable_scope.
+
+ Returns:
+ A dictionary from components of the network to the corresponding activation.
+
+ Raises:
+ ValueError: if final_endpoint is not set to one of the predefined values.
+ """
+ end_points = {}
+ with tf.variable_scope(scope, 'InceptionV1', [inputs]):
+ with slim.arg_scope(
+ [slim.conv2d, slim.fully_connected],
+ weights_initializer=trunc_normal(0.01)):
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ end_point = 'Conv2d_1a_7x7'
+ net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+ end_point = 'MaxPool_2a_3x3'
+ net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+ end_point = 'Conv2d_2b_1x1'
+ net = slim.conv2d(net, 64, [1, 1], scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+ end_point = 'Conv2d_2c_3x3'
+ net = slim.conv2d(net, 192, [3, 3], scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+ end_point = 'MaxPool_3a_3x3'
+ net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_3b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_3c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'MaxPool_4a_3x3'
+ net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_4b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_4c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_4d'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_4e'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_4f'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'MaxPool_5a_2x2'
+ net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_5b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+
+ end_point = 'Mixed_5c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if final_endpoint == end_point: return net, end_points
+ raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v1(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.8,
+ prediction_fn=slim.softmax,
+ spatial_squeeze=True,
+ reuse=None,
+ scope='InceptionV1'):
+ """Defines the Inception V1 architecture.
+
+ This architecture is defined in:
+
+ Going deeper with convolutions
+ Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+ Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
+ http://arxiv.org/pdf/1409.4842v1.pdf.
+
+ The default image size used to train this network is 224x224.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether is training or not.
+ dropout_keep_prob: the percentage of activation values that are retained.
+ prediction_fn: a function to get predictions out of logits.
+ spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+ of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+ 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 pre-softmax activations, a tensor of size
+ [batch_size, num_classes]
+ end_points: a dictionary from components of the network to the corresponding
+ activation.
+ """
+ # Final pooling and prediction
+ with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes],
+ reuse=reuse) as scope:
+ with slim.arg_scope([slim.batch_norm, slim.dropout],
+ is_training=is_training):
+ net, end_points = inception_v1_base(inputs, scope=scope)
+ with tf.variable_scope('Logits'):
+ net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7')
+ net = slim.dropout(net,
+ dropout_keep_prob, scope='Dropout_0b')
+ logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+ normalizer_fn=None, scope='Conv2d_0c_1x1')
+ if spatial_squeeze:
+ logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+
+ end_points['Logits'] = logits
+ end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+ return logits, end_points
+inception_v1.default_image_size = 224
+
+inception_v1_arg_scope = inception_utils.inception_arg_scope
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