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inception_resnet_v2.py
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inception_resnet_v2.py
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from keras.layers import Input, merge, Dropout, Dense, Lambda, Flatten, Activation
from keras.layers.convolutional import MaxPooling2D, Convolution2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import backend as K
import warnings
warnings.filterwarnings('ignore')
"""
Implementation of Inception-Residual Network v1 [Inception Network v4 Paper](http://arxiv.org/pdf/1602.07261v1.pdf) in Keras.
Some additional details:
[1] Each of the A, B and C blocks have a 'scale_residual' parameter.
The scale residual parameter is according to the paper. It is however turned OFF by default.
Simply setting 'scale=True' in the create_inception_resnet_v2() method will add scaling.
[2] There were minor inconsistencies with filter size in both B and C blocks.
In the B blocks: 'ir_conv' nb of filters is given as 1154, however input size is 1152.
This causes inconsistencies in the merge-add mode, therefore the 'ir_conv' filter size
is reduced to 1152 to match input size.
In the C blocks: 'ir_conv' nb of filter is given as 2048, however input size is 2144.
This causes inconsistencies in the merge-add mode, therefore the 'ir_conv' filter size
is increased to 2144 to match input size.
Currently trying to find a proper solution with original nb of filters.
[3] In the stem function, the last Convolutional2D layer has 384 filters instead of the original 256.
This is to correctly match the nb of filters in 'ir_conv' of the next A blocks.
"""
def inception_resnet_stem(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
c = Convolution2D(32, 3, 3, activation='relu', subsample=(2, 2))(input)
c = Convolution2D(32, 3, 3, activation='relu', )(c)
c = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(c)
c1 = MaxPooling2D((3, 3), strides=(2, 2))(c)
c2 = Convolution2D(96, 3, 3, activation='relu', subsample=(2, 2))(c)
m = merge([c1, c2], mode='concat', concat_axis=channel_axis)
c1 = Convolution2D(64, 1, 1, activation='relu', border_mode='same')(m)
c1 = Convolution2D(96, 3, 3, activation='relu', )(c1)
c2 = Convolution2D(64, 1, 1, activation='relu', border_mode='same')(m)
c2 = Convolution2D(64, 7, 1, activation='relu', border_mode='same')(c2)
c2 = Convolution2D(64, 1, 7, activation='relu', border_mode='same')(c2)
c2 = Convolution2D(96, 3, 3, activation='relu', border_mode='valid')(c2)
m2 = merge([c1, c2], mode='concat', concat_axis=channel_axis)
p1 = MaxPooling2D((3, 3), strides=(2, 2), )(m2)
p2 = Convolution2D(192, 3, 3, activation='relu', subsample=(2, 2))(m2)
m3 = merge([p1, p2], mode='concat', concat_axis=channel_axis)
m3 = BatchNormalization(axis=channel_axis)(m3)
m3 = Activation('relu')(m3)
return m3
def inception_resnet_v2_A(input, scale_residual=True):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input is relu activation
init = input
ir1 = Convolution2D(32, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(32, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(ir2)
ir3 = Convolution2D(32, 1, 1, activation='relu', border_mode='same')(input)
ir3 = Convolution2D(48, 3, 3, activation='relu', border_mode='same')(ir3)
ir3 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(ir3)
ir_merge = merge([ir1, ir2, ir3], concat_axis=channel_axis, mode='concat')
ir_conv = Convolution2D(384, 1, 1, activation='linear', border_mode='same')(ir_merge)
if scale_residual: ir_conv = Lambda(lambda x: x * 0.1)(ir_conv)
out = merge([init, ir_conv], mode='sum')
out = BatchNormalization(axis=channel_axis)(out)
out = Activation("relu")(out)
return out
def inception_resnet_v2_B(input, scale_residual=True):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input is relu activation
init = input
ir1 = Convolution2D(192, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(128, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(160, 1, 7, activation='relu', border_mode='same')(ir2)
ir2 = Convolution2D(192, 7, 1, activation='relu', border_mode='same')(ir2)
ir_merge = merge([ir1, ir2], mode='concat', concat_axis=channel_axis)
ir_conv = Convolution2D(1152, 1, 1, activation='linear', border_mode='same')(ir_merge)
if scale_residual: ir_conv = Lambda(lambda x: x * 0.1)(ir_conv)
out = merge([init, ir_conv], mode='sum')
out = BatchNormalization(axis=channel_axis)(out)
out = Activation("relu")(out)
return out
def inception_resnet_v2_C(input, scale_residual=True):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input is relu activation
init = input
ir1 = Convolution2D(192, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(192, 1, 1, activation='relu', border_mode='same')(input)
ir2 = Convolution2D(224, 1, 3, activation='relu', border_mode='same')(ir2)
ir2 = Convolution2D(256, 3, 1, activation='relu', border_mode='same')(ir2)
ir_merge = merge([ir1, ir2], mode='concat', concat_axis=channel_axis)
ir_conv = Convolution2D(2144, 1, 1, activation='linear', border_mode='same')(ir_merge)
if scale_residual: ir_conv = Lambda(lambda x: x * 0.1)(ir_conv)
out = merge([init, ir_conv], mode='sum')
out = BatchNormalization(axis=channel_axis)(out)
out = Activation("relu")(out)
return out
def reduction_A(input, k=192, l=224, m=256, n=384):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = MaxPooling2D((3,3), strides=(2,2))(input)
r2 = Convolution2D(n, 3, 3, activation='relu', subsample=(2,2))(input)
r3 = Convolution2D(k, 1, 1, activation='relu', border_mode='same')(input)
r3 = Convolution2D(l, 3, 3, activation='relu', border_mode='same')(r3)
r3 = Convolution2D(m, 3, 3, activation='relu', subsample=(2,2))(r3)
m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
m = BatchNormalization(axis=1)(m)
m = Activation('relu')(m)
return m
def reduction_resnet_v2_B(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)
r2 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
r2 = Convolution2D(384, 3, 3, activation='relu', subsample=(2,2))(r2)
r3 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
r3 = Convolution2D(288, 3, 3, activation='relu', subsample=(2, 2))(r3)
r4 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
r4 = Convolution2D(288, 3, 3, activation='relu', border_mode='same')(r4)
r4 = Convolution2D(320, 3, 3, activation='relu', subsample=(2, 2))(r4)
m = merge([r1, r2, r3, r4], concat_axis=channel_axis, mode='concat')
m = BatchNormalization(axis=channel_axis)(m)
m = Activation('relu')(m)
return m
def create_inception_resnet_v2(nb_classes=1001, scale=True):
'''
Creates a inception resnet v2 network
:param nb_classes: number of classes.txt
:param scale: flag to add scaling of activations
:return: Keras Model with 1 input (299x299x3) input shape and 2 outputs (final_output, auxiliary_output)
'''
if K.image_dim_ordering() == 'th':
init = Input((3, 299, 299))
else:
init = Input((299, 299, 3))
# Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
x = inception_resnet_stem(init)
# 10 x Inception Resnet A
for i in range(10):
x = inception_resnet_v2_A(x, scale_residual=scale)
# Reduction A
x = reduction_A(x, k=256, l=256, m=384, n=384)
# 20 x Inception Resnet B
for i in range(20):
x = inception_resnet_v2_B(x, scale_residual=scale)
# Auxiliary tower
aux_out = AveragePooling2D((5, 5), strides=(3, 3))(x)
aux_out = Convolution2D(128, 1, 1, border_mode='same', activation='relu')(aux_out)
aux_out = Convolution2D(768, 5, 5, activation='relu')(aux_out)
aux_out = Flatten()(aux_out)
aux_out = Dense(nb_classes, activation='softmax')(aux_out)
# Reduction Resnet B
x = reduction_resnet_v2_B(x)
# 10 x Inception Resnet C
for i in range(10):
x = inception_resnet_v2_C(x, scale_residual=scale)
# Average Pooling
x = AveragePooling2D((8,8))(x)
# Dropout
x = Dropout(0.8)(x)
x = Flatten()(x)
# Output
out = Dense(output_dim=nb_classes, activation='softmax')(x)
model = Model(init, output=[out, aux_out], name='Inception-Resnet-v2')
return model
if __name__ == "__main__":
from keras.utils.visualize_util import plot
inception_resnet_v2 = create_inception_resnet_v2()
#inception_resnet_v2.summary()
plot(inception_resnet_v2, to_file="Inception ResNet-v2.png", show_shapes=True)