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InceptionResnetV2.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.
#
# Modified by Robert Csordas, 2017.
# ==============================================================================
import tensorflow as tf
import tensorflow.contrib.slim as slim
from Utils import CheckpointLoader
class InceptionResnetV2:
@staticmethod
def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, activation_fn=None, scope='Conv2d_1x1')
net += scale * up
if activation_fn:
net = activation_fn(net)
return net
@staticmethod
def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 17x17 resnet block."""
with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7], scope='Conv2d_0b_1x7')
tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1], scope='Conv2d_0c_7x1')
mixed = tf.concat([tower_conv, tower_conv1_2], 3)
up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, activation_fn=None, scope='Conv2d_1x1')
net += scale * up
if activation_fn:
net = activation_fn(net)
return net
@staticmethod
def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 8x8 resnet block."""
with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3], scope='Conv2d_0b_1x3')
tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1], scope='Conv2d_0c_3x1')
mixed = tf.concat([tower_conv, tower_conv1_2], 3)
up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, activation_fn=None, scope='Conv2d_1x1')
net += scale * up
if activation_fn:
net = activation_fn(net)
return net
@staticmethod
def define(inputs, reuse, weightDecay, scope='InceptionResnetV2', trainFrom=None, freezeBatchNorm=False):
"""Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
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 logits outputs of the model.
end_points: the set of end_points from the inception model.
"""
with tf.name_scope('preprocess'):
#BGR -> RGB
inputs = tf.reverse(inputs, axis=[3])
#Normalize
inputs = 2.0*(inputs/255.0 - 0.5)
end_points = {}
scopes = []
trainBatchNormScope = slim.arg_scope([slim.batch_norm], is_training=True)
weightDecayScope = slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weightDecay),
biases_regularizer=slim.l2_regularizer(weightDecay))
trainBnEntered = False
currBlock = ""
def beginBlock(name):
nonlocal trainBnEntered
nonlocal currBlock
currBlock = name
if (trainFrom is not None) and (not trainBnEntered) and (trainFrom==name or trainFrom=="start"):
print("Enabling training on "+trainFrom)
if not freezeBatchNorm:
trainBatchNormScope.__enter__()
weightDecayScope.__enter__()
trainBnEntered=True
def endBlock(net, scope=True, name=None):
if name is None:
name = currBlock
end_points[name]=net
if scope:
scopes.append(name)
def endAll():
if trainBnEntered:
if not freezeBatchNorm:
trainBatchNormScope.__exit__(None, None, None)
weightDecayScope.__exit__(None,None,None)
with tf.variable_scope(scope, 'InceptionResnetV2', [inputs], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm], is_training=False):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
# 149 x 149 x 32
beginBlock('Conv2d_1a_3x3')
net = slim.conv2d(inputs, 32, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
endBlock(net)
# 147 x 147 x 32
beginBlock('Conv2d_2a_3x3')
net = slim.conv2d(net, 32, 3, padding='VALID', scope='Conv2d_2a_3x3')
endBlock(net)
# 147 x 147 x 64
beginBlock('Conv2d_2b_3x3')
net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
endBlock(net)
# 73 x 73 x 64
beginBlock('MaxPool_3a_3x3')
net = slim.max_pool2d(net, 3, stride=2, padding='VALID', scope='MaxPool_3a_3x3')
endBlock(net)
# 73 x 73 x 80
beginBlock('Conv2d_3b_1x1')
net = slim.conv2d(net, 80, 1, padding='VALID', scope='Conv2d_3b_1x1')
endBlock(net)
# 71 x 71 x 192
beginBlock('Conv2d_4a_3x3')
net = slim.conv2d(net, 192, 3, padding='VALID', scope='Conv2d_4a_3x3')
endBlock(net)
# 35 x 35 x 192
beginBlock('MaxPool_5a_3x3')
net = slim.max_pool2d(net, 3, stride=2, padding='VALID', scope='MaxPool_5a_3x3')
endBlock(net)
# 35 x 35 x 320
beginBlock('Mixed_5b')
with tf.variable_scope('Mixed_5b'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5, scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3, scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3, scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME', scope='AvgPool_0a_3x3')
tower_pool_1 = slim.conv2d(tower_pool, 64, 1, scope='Conv2d_0b_1x1')
net = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
endBlock(net)
beginBlock('Repeat')
net = slim.repeat(net, 10, InceptionResnetV2.block35, scale=0.17)
endBlock(net)
# 17 x 17 x 1024
beginBlock('Mixed_6a')
with tf.variable_scope('Mixed_6a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 384, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3, scope='Conv2d_0b_3x3')
tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID', scope='MaxPool_1a_3x3')
net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
endBlock(net)
beginBlock('Repeat_1')
net = slim.repeat(net, 20, InceptionResnetV2.block17, scale=0.10)
endBlock(net)
endBlock(net, scope=False, name='aux')
beginBlock('Mixed_7a')
with tf.variable_scope('Mixed_7a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3, scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2, padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID', scope='MaxPool_1a_3x3')
net = tf.concat([tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
endBlock(net)
beginBlock('Repeat_2')
net = slim.repeat(net, 9, InceptionResnetV2.block8, scale=0.20)
endBlock(net)
beginBlock('Block8')
net = InceptionResnetV2.block8(net, activation_fn=None)
endBlock(net)
beginBlock('Conv2d_7b_1x1')
net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
endBlock(net)
endBlock(net, scope=False, name='PrePool')
endAll()
return end_points, scope, scopes
def __init__(self, name, inputs, trainFrom = None, reuse=False, weightDecay=0.00004, batchNormDecay=0.9997, batchNormEpsilon=0.001, freezeBatchNorm=False):
self.name = name
self.inputs = inputs
self.trainFrom = trainFrom
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=None,
biases_regularizer=None):
batch_norm_params = {
'decay': batchNormDecay,
'epsilon': batchNormEpsilon,
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as scope:
self.endPoints, self.scope, self.scopeList = InceptionResnetV2.define(inputs, weightDecay = weightDecay, trainFrom=trainFrom, scope=name, reuse = reuse, freezeBatchNorm = freezeBatchNorm)
def importWeights(self, sess, filename, includeTraining=False):
ignores = [] if includeTraining or (self.trainFrom is None) else self.getScopes(fromLayer = self.trainFrom, inclusive = True)
print("Ignoring blocks:")
print(ignores)
CheckpointLoader.importIntoScope(sess, filename, fromScope="InceptionResnetV2", toScope=self.scope.name, ignore=ignores)
def getOutput(self, name=None):
if name is None:
return self.endPoints
else:
return self.endPoints[name]
def getScopes(self, fromLayer = None, toLayer = None, inclusive=False):
l=[]
print(fromLayer)
if fromLayer is not None:
assert(toLayer is None)
i = self.scopeList.index(fromLayer)
if not inclusive:
i += 1
l=self.scopeList[i:]
elif toLayer is not None:
assert(fromLayer is None)
i = self.scopeList.index(toLayer)
if not inclusive:
i -= 1
if i<0:
l=[]
else:
l=self.scopeList[:(i+1)]
else:
l=self.scopeList
return [self.scope.name+"/"+s+"/" for s in l]
def getTrainableVars(self):
if self.trainFrom==None:
return []
vars=[]
scopes = self.getScopes(fromLayer=self.trainFrom, inclusive=True)
for s in scopes:
vars += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=s)
return vars