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inception_v4.py
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inception_v4.py
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from keras.layers import Input, merge, Dropout, Dense, 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
from keras.utils.data_utils import get_file
"""
Implementation of Inception Network v4 [Inception Network v4 Paper](http://arxiv.org/pdf/1602.07261v1.pdf) in Keras.
"""
TH_BACKEND_TH_DIM_ORDERING = "https://github.com/titu1994/Inception-v4/releases/download/v1.2/inception_v4_weights_th_dim_ordering_th_kernels.h5"
TH_BACKEND_TF_DIM_ORDERING = "https://github.com/titu1994/Inception-v4/releases/download/v1.2/inception_v4_weights_tf_dim_ordering_th_kernels.h5"
TF_BACKEND_TF_DIM_ORDERING = "https://github.com/titu1994/Inception-v4/releases/download/v1.2/inception_v4_weights_tf_dim_ordering_tf_kernels.h5"
TF_BACKEND_TH_DIM_ORDERING = "https://github.com/titu1994/Inception-v4/releases/download/v1.2/inception_v4_weights_th_dim_ordering_tf_kernels.h5"
def conv_block(x, nb_filter, nb_row, nb_col, border_mode='same', subsample=(1, 1), bias=False):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
x = Convolution2D(nb_filter, nb_row, nb_col, subsample=subsample, border_mode=border_mode, bias=bias)(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
return x
def inception_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)
x = conv_block(input, 32, 3, 3, subsample=(2, 2), border_mode='valid')
x = conv_block(x, 32, 3, 3, border_mode='valid')
x = conv_block(x, 64, 3, 3)
x1 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(x)
x2 = conv_block(x, 96, 3, 3, subsample=(2, 2), border_mode='valid')
x = merge([x1, x2], mode='concat', concat_axis=channel_axis)
x1 = conv_block(x, 64, 1, 1)
x1 = conv_block(x1, 96, 3, 3, border_mode='valid')
x2 = conv_block(x, 64, 1, 1)
x2 = conv_block(x2, 64, 1, 7)
x2 = conv_block(x2, 64, 7, 1)
x2 = conv_block(x2, 96, 3, 3, border_mode='valid')
x = merge([x1, x2], mode='concat', concat_axis=channel_axis)
x1 = conv_block(x, 192, 3, 3, subsample=(2, 2), border_mode='valid')
x2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(x)
x = merge([x1, x2], mode='concat', concat_axis=channel_axis)
return x
def inception_A(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
a1 = conv_block(input, 96, 1, 1)
a2 = conv_block(input, 64, 1, 1)
a2 = conv_block(a2, 96, 3, 3)
a3 = conv_block(input, 64, 1, 1)
a3 = conv_block(a3, 96, 3, 3)
a3 = conv_block(a3, 96, 3, 3)
a4 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
a4 = conv_block(a4, 96, 1, 1)
m = merge([a1, a2, a3, a4], mode='concat', concat_axis=channel_axis)
return m
def inception_B(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
b1 = conv_block(input, 384, 1, 1)
b2 = conv_block(input, 192, 1, 1)
b2 = conv_block(b2, 224, 1, 7)
b2 = conv_block(b2, 256, 7, 1)
b3 = conv_block(input, 192, 1, 1)
b3 = conv_block(b3, 192, 7, 1)
b3 = conv_block(b3, 224, 1, 7)
b3 = conv_block(b3, 224, 7, 1)
b3 = conv_block(b3, 256, 1, 7)
b4 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
b4 = conv_block(b4, 128, 1, 1)
m = merge([b1, b2, b3, b4], mode='concat', concat_axis=channel_axis)
return m
def inception_C(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
c1 = conv_block(input, 256, 1, 1)
c2 = conv_block(input, 384, 1, 1)
c2_1 = conv_block(c2, 256, 1, 3)
c2_2 = conv_block(c2, 256, 3, 1)
c2 = merge([c2_1, c2_2], mode='concat', concat_axis=channel_axis)
c3 = conv_block(input, 384, 1, 1)
c3 = conv_block(c3, 448, 3, 1)
c3 = conv_block(c3, 512, 1, 3)
c3_1 = conv_block(c3, 256, 1, 3)
c3_2 = conv_block(c3, 256, 3, 1)
c3 = merge([c3_1, c3_2], mode='concat', concat_axis=channel_axis)
c4 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
c4 = conv_block(c4, 256, 1, 1)
m = merge([c1, c2, c3, c4], mode='concat', concat_axis=channel_axis)
return m
def reduction_A(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = conv_block(input, 384, 3, 3, subsample=(2, 2), border_mode='valid')
r2 = conv_block(input, 192, 1, 1)
r2 = conv_block(r2, 224, 3, 3)
r2 = conv_block(r2, 256, 3, 3, subsample=(2, 2), border_mode='valid')
r3 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)
m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
return m
def reduction_B(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = conv_block(input, 192, 1, 1)
r1 = conv_block(r1, 192, 3, 3, subsample=(2, 2), border_mode='valid')
r2 = conv_block(input, 256, 1, 1)
r2 = conv_block(r2, 256, 1, 7)
r2 = conv_block(r2, 320, 7, 1)
r2 = conv_block(r2, 320, 3, 3, subsample=(2, 2), border_mode='valid')
r3 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)
m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
return m
def create_inception_v4(nb_classes=1001, load_weights=True):
'''
Creates a inception v4 network
:param nb_classes: number of classes.txt
:return: Keras Model with 1 input and 1 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_stem(init)
# 4 x Inception A
for i in range(4):
x = inception_A(x)
# Reduction A
x = reduction_A(x)
# 7 x Inception B
for i in range(7):
x = inception_B(x)
# Reduction B
x = reduction_B(x)
# 3 x Inception C
for i in range(3):
x = inception_C(x)
# 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, out, name='Inception-v4')
if load_weights:
if K.backend() == "theano":
if K.image_dim_ordering() == "th":
weights = get_file('inception_v4_weights_th_dim_ordering_th_kernels.h5', TH_BACKEND_TH_DIM_ORDERING,
cache_subdir='models')
else:
weights = get_file('inception_v4_weights_tf_dim_ordering_th_kernels.h5', TH_BACKEND_TF_DIM_ORDERING,
cache_subdir='models')
else:
if K.image_dim_ordering() == "th":
weights = get_file('inception_v4_weights_th_dim_ordering_tf_kernels.h5', TF_BACKEND_TH_DIM_ORDERING,
cache_subdir='models')
else:
weights = get_file('inception_v4_weights_tf_dim_ordering_tf_kernels.h5', TH_BACKEND_TF_DIM_ORDERING,
cache_subdir='models')
model.load_weights(weights)
print("Model weights loaded.")
return model
if __name__ == "__main__":
# from keras.utils.visualize_util import plot
inception_v4 = create_inception_v4(load_weights=True)
# inception_v4.summary()
# plot(inception_v4, to_file="Inception-v4.png", show_shapes=True)