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inception.py
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inception.py
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# 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 v3 classification network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import socket
import tensorflow as tf
import numpy as np
import inception_utils
from PIL import Image
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
from multiprocessing import dummy as multiprocessing
def inception_arg_scope(weight_decay=0.00004,
use_batch_norm=True,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu,
batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS):
"""Defines the default arg scope for inception models.
Args:
weight_decay: The weight decay to use for regularizing the model.
use_batch_norm: "If `True`, batch_norm is applied after each convolution.
batch_norm_decay: Decay for batch norm moving average.
batch_norm_epsilon: Small float added to variance to avoid dividing by zero
in batch norm.
activation_fn: Activation function for conv2d.
batch_norm_updates_collections: Collection for the update ops for
batch norm.
Returns:
An `arg_scope` to use for the inception models.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': batch_norm_decay,
# epsilon to prevent 0s in variance.
'epsilon': batch_norm_epsilon,
# collection containing update_ops.
'updates_collections': batch_norm_updates_collections,
# use fused batch norm if possible.
'fused': None,
}
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=activation_fn,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params) as sc:
return sc
def inception_v3_base(inputs,
final_endpoint='Mixed_7c',
min_depth=16,
depth_multiplier=1.0,
scope=None):
"""Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
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.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
start_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with tf.variable_scope(scope, 'InceptionV3', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='VALID'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
start_points[end_point] = inputs
net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point, activation_fn=None)
end_points[end_point] = net
net = tf.nn.relu(net)
if end_point == final_endpoint: return net, end_points
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
start_points[end_point] = net
net = slim.conv2d(net, depth(32), [3, 3], scope=end_point, activation_fn=None)
end_points[end_point] = net
net = tf.nn.relu(net)
if end_point == final_endpoint: return net, end_points
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
start_points[end_point] = net
net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point, activation_fn=None)
end_points[end_point] = net
net = tf.nn.relu(net)
if end_point == final_endpoint: return net, end_points
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
start_points[end_point] = net
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
start_points[end_point] = net
net = slim.conv2d(net, depth(80), [1, 1], scope=end_point, activation_fn=None)
end_points[end_point] = net
net = tf.nn.relu(net)
if end_point == final_endpoint: return net, end_points
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
start_points[end_point] = net
net = slim.conv2d(net, depth(192), [3, 3], scope=end_point, activation_fn=None)
end_points[end_point] = net
net = tf.nn.relu(net)
if end_point == final_endpoint: return net, end_points
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
start_points[end_point] = net
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 35 x 35 x 192.
# Inception blocks
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_5x5'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_5x5'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(32), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0b_1x1'] = net
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv_1_0c_5x5'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv_1_0c_5x5', activation_fn=None)
end_points[end_point + '/Branch_1/Conv_1_0c_5x5'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_5x5'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_5x5'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_1a_1x1'] = net
branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_1a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_3x3'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_3x3'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_1a_1x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_1a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/MaxPool_1a_3x3'] = net
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
end_points[end_point + '/Branch_2/MaxPool_1a_3x3'] = branch_2
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(128), [1, 7], scope='Conv2d_0b_1x7', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], scope='Conv2d_0b_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(128), [1, 7], scope='Conv2d_0c_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], scope='Conv2d_0d_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], scope='Conv2d_0b_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0c_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], scope='Conv2d_0d_7x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_7x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0e_1x7'] = branch_2
branch_2 = tf.nn.relu(branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
start_points[end_point + '/Branch_0/Conv2d_1a_3x3'] = branch_0
branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_1a_3x3'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x7'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_7x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_1a_3x3'] = branch_1
branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_1a_3x3'] = branch_1
branch_1 = tf.nn.relu(branch_1)
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/MaxPool_1a_3x3'] = net
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
end_points[end_point + '/Branch_2/MaxPool_1a_3x3'] = branch_2
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x3'] = branch_1
branch_1_a = slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x3'] = branch_1_a
branch_1_a = tf.nn.relu(branch_1_a)
start_points[end_point + '/Branch_1/Conv2d_0b_3x1'] = branch_1
branch_1_b = slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_3x1'] = branch_1_b
branch_1_b = tf.nn.relu(branch_1_b)
branch_1 = tf.concat(axis=3, values=[branch_1_a, branch_1_b])
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x3'] = branch_2
branch_2_a = slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x3'] = branch_2_a
branch_2_a = tf.nn.relu(branch_2_a)
start_points[end_point + '/Branch_2/Conv2d_0d_3x1'] = branch_2
branch_2_b = slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_3x1'] = branch_2_b
branch_2_b = tf.nn.relu(branch_2_b)
branch_2 = tf.concat(axis=3, values=[branch_2_a, branch_2_b])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
start_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = net
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_0/Conv2d_0a_1x1'] = branch_0
branch_0 = tf.nn.relu(branch_0)
with tf.variable_scope('Branch_1'):
start_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = net
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0a_1x1'] = branch_1
branch_1 = tf.nn.relu(branch_1)
start_points[end_point + '/Branch_1/Conv2d_0b_1x3'] = branch_1
branch_1_a = slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0b_1x3'] = branch_1_a
branch_1_a = tf.nn.relu(branch_1_a)
start_points[end_point + '/Branch_1/Conv2d_0c_3x1'] = branch_1
branch_1_b = slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1', activation_fn=None)
end_points[end_point + '/Branch_1/Conv2d_0c_3x1'] = branch_1_b
branch_1_b = tf.nn.relu(branch_1_b)
branch_1 = tf.concat(axis=3, values=[branch_1_a, branch_1_b])
with tf.variable_scope('Branch_2'):
start_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = net
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0a_1x1'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = slim.conv2d(branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0b_3x3'] = branch_2
branch_2 = tf.nn.relu(branch_2)
start_points[end_point + '/Branch_2/Conv2d_0c_1x3'] = branch_2
branch_2_a = slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0c_1x3'] = branch_2_a
branch_2_a = tf.nn.relu(branch_2_a)
start_points[end_point + '/Branch_2/Conv2d_0d_3x1'] = branch_2
branch_2_b = slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1', activation_fn=None)
end_points[end_point + '/Branch_2/Conv2d_0d_3x1'] = branch_2_b
branch_2_b = tf.nn.relu(branch_2_b)
branch_2 = tf.concat(axis=3, values=[branch_2_a, branch_2_b])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
start_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1', activation_fn=None)
end_points[end_point + '/Branch_3/Conv2d_0b_1x1'] = branch_3
branch_3 = tf.nn.relu(branch_3)
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points, start_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v3(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,
create_aux_logits=True,
scope='InceptionV3',
global_pool=False):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes. If 0 or None, the logits layer
is omitted and the input features to the logits layer (before dropout)
are returned instead.
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 [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.
create_aux_logits: Whether to create the auxiliary logits.
scope: Optional variable_scope.
global_pool: Optional boolean flag to control the avgpooling before the
logits layer. If false or unset, pooling is done with a fixed window
that reduces default-sized inputs to 1x1, while larger inputs lead to
larger outputs. If true, any input size is pooled down to 1x1.
Returns:
net: a Tensor with the logits (pre-softmax activations) if num_classes
is a non-zero integer, or the non-dropped-out input to the logits layer
if num_classes is 0 or None.
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with tf.variable_scope(scope, 'InceptionV3', [inputs], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points, start_points = inception_v3_base(
inputs, scope=scope, min_depth=min_depth,
depth_multiplier=depth_multiplier)
# Final pooling and prediction
with tf.variable_scope('Logits'):
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='GlobalPool')
end_points['global_pool'] = net
else:
# Pooling with a fixed kernel size.
kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
end_points['AvgPool_1a'] = net
if not num_classes:
return net, end_points
# 1 x 1 x 2048
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = slim.conv2d(net, num_classes, [1, 1], normalizer_fn=None, scope='Conv2d_1c_1x1', activation_fn=None)
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
# 1000
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points, start_points
inception_v3.default_image_size = 299
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.stack([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
class InceptionV3Wrapper_public(object):
def __init__(self, sess, saver, input_tensor, labels_tensor, end_points, start_points, labels_path):
BOTTLENECK_DIC = {'Mixed_5c':'mixed_1',
'Mixed_5d':'mixed_2',
'Mixed_6a':'mixed_3',
'Mixed_6b':'mixed_4',
'Mixed_6c':'mixed_5',
'Mixed_6d':'mixed_6',
'Mixed_6e':'mixed_7',
'Mixed_7a':'mixed_8',
'Mixed_7b':'mixed_9',
'Mixed_7c':'mixed_10'}
ENDS_DIC = {'Predictions':'prediction',
'Logits':'logits',
'PreLogits':'prelogits',
'Mixed_7c':'pre_avgpool'}
self.labels = tf.gfile.Open(labels_path).read().splitlines()
self.y_input = labels_tensor
self.bottlenecks_tensors={}