+++ /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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