341 lines
16 KiB
Python
341 lines
16 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the definition for inception v1 classification network."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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slim = tf.contrib.slim
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trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
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def inception_v1_base(inputs,
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final_endpoint='Mixed_5c',
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scope='InceptionV1'):
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"""Defines the Inception V1 base architecture.
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This architecture is defined in:
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Going deeper with convolutions
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
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Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
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http://arxiv.org/pdf/1409.4842v1.pdf.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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final_endpoint: specifies the endpoint to construct the network up to. It
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can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
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'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
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'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
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'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
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scope: Optional variable_scope.
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Returns:
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A dictionary from components of the network to the corresponding activation.
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Raises:
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ValueError: if final_endpoint is not set to one of the predefined values.
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"""
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end_points = {}
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with tf.variable_scope(scope, 'InceptionV1', [inputs]):
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with slim.arg_scope(
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[slim.conv2d, slim.fully_connected],
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weights_initializer=trunc_normal(0.01)):
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with slim.arg_scope([slim.conv2d, slim.max_pool2d],
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stride=1, padding='SAME'):
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end_point = 'Conv2d_1a_7x7'
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net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'MaxPool_2a_3x3'
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net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Conv2d_2b_1x1'
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net = slim.conv2d(net, 64, [1, 1], scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Conv2d_2c_3x3'
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net = slim.conv2d(net, 192, [3, 3], scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'MaxPool_3a_3x3'
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net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_3b'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_3c'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'MaxPool_4a_3x3'
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net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_4b'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_4c'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_4d'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_4e'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_4f'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'MaxPool_5a_2x2'
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net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_5b'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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end_point = 'Mixed_5c'
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with tf.variable_scope(end_point):
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with tf.variable_scope('Branch_0'):
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branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
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branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
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net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
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end_points[end_point] = net
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if final_endpoint == end_point: return net, end_points
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raise ValueError('Unknown final endpoint %s' % final_endpoint)
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def inception_v1(inputs,
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num_classes=1000,
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is_training=True,
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dropout_keep_prob=0.8,
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prediction_fn=slim.softmax,
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spatial_squeeze=True,
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reuse=None,
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scope='InceptionV1'):
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"""Defines the Inception V1 architecture.
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This architecture is defined in:
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Going deeper with convolutions
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
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Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
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http://arxiv.org/pdf/1409.4842v1.pdf.
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The default image size used to train this network is 224x224.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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num_classes: number of predicted classes.
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is_training: whether is training or not.
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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 is [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='MaxPool_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
|
||
|
|
|
||
|
|
|
||
|
|
def inception_v1_arg_scope(weight_decay=0.00004,
|
||
|
|
use_batch_norm=True):
|
||
|
|
"""Defines the default InceptionV1 arg scope.
|
||
|
|
|
||
|
|
Note: Althougth the original paper didn't use batch_norm we found it useful.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
weight_decay: The weight decay to use for regularizing the model.
|
||
|
|
use_batch_norm: "If `True`, batch_norm is applied after each convolution.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An `arg_scope` to use for the inception v3 model.
|
||
|
|
"""
|
||
|
|
batch_norm_params = {
|
||
|
|
# Decay for the moving averages.
|
||
|
|
'decay': 0.9997,
|
||
|
|
# epsilon to prevent 0s in variance.
|
||
|
|
'epsilon': 0.001,
|
||
|
|
# collection containing update_ops.
|
||
|
|
'updates_collections': tf.GraphKeys.UPDATE_OPS,
|
||
|
|
}
|
||
|
|
if use_batch_norm:
|
||
|
|
normalizer_fn = slim.batch_norm
|
||
|
|
normalizer_params = batch_norm_params
|
||
|
|
else:
|
||
|
|
normalizer_fn = None
|
||
|
|
normalizer_params = {}
|
||
|
|
# Set weight_decay for weights in Conv and FC layers.
|
||
|
|
with slim.arg_scope([slim.conv2d, slim.fully_connected],
|
||
|
|
weights_regularizer=slim.l2_regularizer(weight_decay)):
|
||
|
|
with slim.arg_scope(
|
||
|
|
[slim.conv2d],
|
||
|
|
weights_initializer=slim.variance_scaling_initializer(),
|
||
|
|
activation_fn=tf.nn.relu,
|
||
|
|
normalizer_fn=normalizer_fn,
|
||
|
|
normalizer_params=normalizer_params) as sc:
|
||
|
|
return sc
|