546 lines
24 KiB
Python
546 lines
24 KiB
Python
# 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 v2 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_v2_base(inputs,
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final_endpoint='Mixed_5c',
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min_depth=16,
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depth_multiplier=1.0,
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scope=None):
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"""Inception v2 (6a2).
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Constructs an Inception v2 network from inputs to the given final endpoint.
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This method can construct the network up to the layer inception(5b) as
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described in http://arxiv.org/abs/1502.03167.
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Args:
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inputs: a tensor of shape [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', 'Mixed_4a',
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'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b',
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'Mixed_5c'].
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min_depth: Minimum depth value (number of channels) for all convolution ops.
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Enforced when depth_multiplier < 1, and not an active constraint when
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depth_multiplier >= 1.
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depth_multiplier: Float multiplier for the depth (number of channels)
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for all convolution ops. The value must be greater than zero. Typical
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usage will be to set this value in (0, 1) to reduce the number of
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parameters or computation cost of the model.
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scope: Optional variable_scope.
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Returns:
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tensor_out: output tensor corresponding to the final_endpoint.
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end_points: a set of activations for external use, for example summaries or
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losses.
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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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or depth_multiplier <= 0
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"""
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# end_points will collect relevant activations for external use, for example
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# summaries or losses.
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end_points = {}
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# Used to find thinned depths for each layer.
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if depth_multiplier <= 0:
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raise ValueError('depth_multiplier is not greater than zero.')
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depth = lambda d: max(int(d * depth_multiplier), min_depth)
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with tf.variable_scope(scope, 'InceptionV2', [inputs]):
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with slim.arg_scope(
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[slim.conv2d, slim.max_pool2d, slim.avg_pool2d, slim.separable_conv2d],
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stride=1, padding='SAME'):
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# Note that sizes in the comments below assume an input spatial size of
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# 224x224, however, the inputs can be of any size greater 32x32.
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# 224 x 224 x 3
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end_point = 'Conv2d_1a_7x7'
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# depthwise_multiplier here is different from depth_multiplier.
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# depthwise_multiplier determines the output channels of the initial
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# depthwise conv (see docs for tf.nn.separable_conv2d), while
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# depth_multiplier controls the # channels of the subsequent 1x1
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# convolution. Must have
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# in_channels * depthwise_multipler <= out_channels
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# so that the separable convolution is not overparameterized.
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depthwise_multiplier = min(int(depth(64) / 3), 8)
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net = slim.separable_conv2d(
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inputs, depth(64), [7, 7], depth_multiplier=depthwise_multiplier,
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stride=2, weights_initializer=trunc_normal(1.0),
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scope=end_point)
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 112 x 112 x 64
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end_point = 'MaxPool_2a_3x3'
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net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 56 x 56 x 64
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end_point = 'Conv2d_2b_1x1'
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net = slim.conv2d(net, depth(64), [1, 1], scope=end_point,
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weights_initializer=trunc_normal(0.1))
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 56 x 56 x 64
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end_point = 'Conv2d_2c_3x3'
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net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 56 x 56 x 192
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end_point = 'MaxPool_3a_3x3'
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net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 28 x 28 x 192
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# Inception module.
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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, depth(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(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(64), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(32), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 28 x 28 x 256
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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, depth(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(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 28 x 28 x 320
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end_point = 'Mixed_4a'
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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(
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net, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2,
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(
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branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
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branch_1 = slim.conv2d(
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branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.max_pool2d(
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net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
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net = tf.concat(3, [branch_0, branch_1, branch_2])
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 14 x 14 x 576
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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, depth(224), [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(
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net, depth(64), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(
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branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(96), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 14 x 14 x 576
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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, depth(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(
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net, depth(96), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(128), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(96), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 14 x 14 x 576
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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, depth(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(
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net, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(160), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(96), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 14 x 14 x 576
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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, depth(96), [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(
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net, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(192), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(160), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(96), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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# 14 x 14 x 576
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end_point = 'Mixed_5a'
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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(
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net, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2,
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(
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net, depth(192), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(256), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2,
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scope='Conv2d_1a_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
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scope='MaxPool_1a_3x3')
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net = tf.concat(3, [branch_0, branch_1, branch_2])
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end_points[end_point] = net
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if end_point == final_endpoint: return net, end_points
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# 7 x 7 x 1024
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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, depth(352), [1, 1], scope='Conv2d_0a_1x1')
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with tf.variable_scope('Branch_1'):
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branch_1 = slim.conv2d(
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net, depth(192), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
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scope='Conv2d_0b_3x3')
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with tf.variable_scope('Branch_2'):
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branch_2 = slim.conv2d(
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net, depth(160), [1, 1],
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weights_initializer=trunc_normal(0.09),
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scope='Conv2d_0a_1x1')
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branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
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scope='Conv2d_0b_3x3')
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branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
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scope='Conv2d_0c_3x3')
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with tf.variable_scope('Branch_3'):
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branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
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branch_3 = slim.conv2d(
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branch_3, depth(128), [1, 1],
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weights_initializer=trunc_normal(0.1),
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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 end_point == final_endpoint: return net, end_points
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|
|
|
# 7 x 7 x 1024
|
|
end_point = 'Mixed_5c'
|
|
with tf.variable_scope(end_point):
|
|
with tf.variable_scope('Branch_0'):
|
|
branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
|
|
with tf.variable_scope('Branch_1'):
|
|
branch_1 = slim.conv2d(
|
|
net, depth(192), [1, 1],
|
|
weights_initializer=trunc_normal(0.09),
|
|
scope='Conv2d_0a_1x1')
|
|
branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
|
|
scope='Conv2d_0b_3x3')
|
|
with tf.variable_scope('Branch_2'):
|
|
branch_2 = slim.conv2d(
|
|
net, depth(192), [1, 1],
|
|
weights_initializer=trunc_normal(0.09),
|
|
scope='Conv2d_0a_1x1')
|
|
branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
|
|
scope='Conv2d_0b_3x3')
|
|
branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
|
|
scope='Conv2d_0c_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, depth(128), [1, 1],
|
|
weights_initializer=trunc_normal(0.1),
|
|
scope='Conv2d_0b_1x1')
|
|
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
|
|
end_points[end_point] = net
|
|
if end_point == final_endpoint: return net, end_points
|
|
raise ValueError('Unknown final endpoint %s' % final_endpoint)
|
|
|
|
|
|
def inception_v2(inputs,
|
|
num_classes=1000,
|
|
is_training=True,
|
|
dropout_keep_prob=0.8,
|
|
min_depth=16,
|
|
depth_multiplier=1.0,
|
|
prediction_fn=slim.softmax,
|
|
spatial_squeeze=True,
|
|
reuse=None,
|
|
scope='InceptionV2'):
|
|
"""Inception v2 model for classification.
|
|
|
|
Constructs an Inception v2 network for classification as described in
|
|
http://arxiv.org/abs/1502.03167.
|
|
|
|
The default image size used to train this network is 224x224.
|
|
|
|
Args:
|
|
inputs: a tensor of shape [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.
|
|
min_depth: Minimum depth value (number of channels) for all convolution ops.
|
|
Enforced when depth_multiplier < 1, and not an active constraint when
|
|
depth_multiplier >= 1.
|
|
depth_multiplier: Float multiplier for the depth (number of channels)
|
|
for all convolution ops. The value must be greater than zero. Typical
|
|
usage will be to set this value in (0, 1) to reduce the number of
|
|
parameters or computation cost of the model.
|
|
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.
|
|
|
|
Raises:
|
|
ValueError: if final_endpoint is not set to one of the predefined values,
|
|
or depth_multiplier <= 0
|
|
"""
|
|
if depth_multiplier <= 0:
|
|
raise ValueError('depth_multiplier is not greater than zero.')
|
|
|
|
# Final pooling and prediction
|
|
with tf.variable_scope(scope, 'InceptionV2', [inputs, num_classes],
|
|
reuse=reuse) as scope:
|
|
with slim.arg_scope([slim.batch_norm, slim.dropout],
|
|
is_training=is_training):
|
|
net, end_points = inception_v2_base(
|
|
inputs, scope=scope, min_depth=min_depth,
|
|
depth_multiplier=depth_multiplier)
|
|
with tf.variable_scope('Logits'):
|
|
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
|
|
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
|
|
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
|
|
# 1 x 1 x 1024
|
|
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
|
|
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
|
|
normalizer_fn=None, scope='Conv2d_1c_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_v2.default_image_size = 224
|
|
|
|
|
|
def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
|
|
"""Define kernel size which is automatically reduced for small input.
|
|
|
|
If the shape of the input images is unknown at graph construction time this
|
|
function assumes that the input images are is large enough.
|
|
|
|
Args:
|
|
input_tensor: input tensor of size [batch_size, height, width, channels].
|
|
kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
|
|
|
|
Returns:
|
|
a tensor with the kernel size.
|
|
|
|
TODO(jrru): Make this function work with unknown shapes. Theoretically, this
|
|
can be done with the code below. Problems are two-fold: (1) If the shape was
|
|
known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot
|
|
handle tensors that define the kernel size.
|
|
shape = tf.shape(input_tensor)
|
|
return = tf.pack([tf.minimum(shape[1], kernel_size[0]),
|
|
tf.minimum(shape[2], kernel_size[1])])
|
|
|
|
"""
|
|
shape = input_tensor.get_shape().as_list()
|
|
if shape[1] is None or shape[2] is None:
|
|
kernel_size_out = kernel_size
|
|
else:
|
|
kernel_size_out = [min(shape[1], kernel_size[0]),
|
|
min(shape[2], kernel_size[1])]
|
|
return kernel_size_out
|
|
|
|
|
|
def inception_v2_arg_scope(weight_decay=0.00004):
|
|
"""Defines the default InceptionV2 arg scope.
|
|
|
|
Args:
|
|
weight_decay: The weight decay to use for regularizing the model.
|
|
|
|
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,
|
|
}
|
|
|
|
# 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=slim.batch_norm,
|
|
normalizer_params=batch_norm_params) as sc:
|
|
return sc
|