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ror.py
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/
ror.py
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from keras.models import Model
from keras.layers import Input, Add, Activation, Dropout, Flatten, Dense
from keras.layers.convolutional import Convolution2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras import backend as K
def initial_conv(input):
x = Convolution2D(16, (3, 3), padding='same', kernel_initializer='he_normal')(input)
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
return x
def conv1_block(input, dropout=0.0, initial=False):
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
# Check if input number of filters is same as 16, else create convolution2d for this input
if initial:
if K.image_data_format() == "th":
init = Convolution2D(16, (1, 1), kernel_initializer='he_normal', padding='same')(init)
else:
init = Convolution2D(16, (1, 1), kernel_initializer='he_normal', padding='same')(init)
x = Convolution2D(16, (3, 3), kernel_initializer='he_normal', padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0: x = Dropout(dropout)(x)
x = Convolution2D(16, (3, 3), kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
m = Add()([init, x])
x = Activation('relu')(m)
return x
def conv2_block(input, dropout=0.0):
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
# Check if input number of filters is same as 32, else create convolution2d for this input
if K.image_data_format() == "channels_first":
if init._keras_shape[1] != 32:
init = Convolution2D(32, (1, 1), kernel_initializer='he_normal', padding='same')(init)
else:
if init._keras_shape[-1] != 32:
init = Convolution2D(32, (1, 1), kernel_initializer='he_normal', padding='same')(init)
x = Convolution2D(32, (3, 3), kernel_initializer='he_normal', padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0: x = Dropout(dropout)(x)
x = Convolution2D(32, (3, 3), kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
m = Add()([init, x])
x = Activation('relu')(m)
return x
def conv3_block(input, dropout=0.0, is_last=False):
global count
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
# Check if input number of filters is same as 64, else create convolution2d for this input
if K.image_data_format() == "channels_first":
if init._keras_shape[1] != 64:
init = Convolution2D(64, (1, 1), kernel_initializer='he_normal', padding='same')(init)
else:
if init._keras_shape[-1] != 64:
init = Convolution2D(64, (1, 1), kernel_initializer='he_normal', padding='same')(init)
x = Convolution2D(64, (3, 3), kernel_initializer='he_normal', padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0: x = Dropout(dropout)(x)
x = Convolution2D(64, (3, 3), kernel_initializer='he_normal', padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
m = Add()([init, x])
if not is_last:
m = Activation('relu')(m)
return m
def create_residual_of_residual(input_dim, nb_classes=100, N=2, dropout=0.0, verbose=1):
"""
Creates a Residual Network of Residual Network with specified parameters
Example : To create a RoR-3-110 model for CIFAR-10:
model = create_pre_residual_of_residual((3, 32, 32), 10, N=2)
Note : The ResNet 101 model is the RoR-3-110 model
:param input: Input Keras object
:param nb_classes: Number of output classes
:param N: Depth of the network. Compute n = 6 * (N * 9 - 1) + 8.
Example1: For a depth of 56, N = 1, n = 6 * (1 * 9 - 1) + 8 = 56
Example2: For a depth of 110, N = 2, n = 6 * (2 * 9 - 1) + 8 = 110
Example3: For a depth of 164, N = 3, N = 6 * (3 * 9 - 1) + 8 = 164
:param dropout: Adds dropout if value is greater than 0.0.
Note : Generally not used in RoR
:param verbose: Debug info to describe created WRN
:return:
"""
ip = Input(shape=input_dim)
x = initial_conv(ip)
nb_conv = 8
conv0_level1_shortcut = Convolution2D(64, (1, 1), kernel_initializer='he_normal', padding='same', strides=(4, 4),
name='conv0_level1_shortcut')(x)
conv1_level2_shortcut = Convolution2D(16, (1, 1), kernel_initializer='he_normal', padding='same',
name='conv1_level2_shortcut')(x)
for i in range(N * 9 - 1):
initial = (i == 0)
x = conv1_block(x, dropout, initial=initial)
nb_conv += 2
# Add Level 2 shortcut
x = Add()([x, conv1_level2_shortcut])
x = Activation('relu')(x)
x = MaxPooling2D((2, 2))(x)
conv2_level2_shortcut = Convolution2D(32, (1, 1), kernel_initializer='he_normal', padding='same',
name='conv2_level2_shortcut')(x)
for i in range(N * 9 - 1):
x = conv2_block(x, dropout)
nb_conv += 2
# Add Level 2 shortcut
x = Add()([x, conv2_level2_shortcut])
x = Activation('relu')(x)
x = MaxPooling2D((2, 2))(x)
conv3_level2_shortcut = Convolution2D(64, (1, 1), kernel_initializer='he_normal', padding='same',
name='conv3_level2_shortcut')(x)
for i in range(N * 9 - 1):
is_last = (i == N - 1)
x = conv3_block(x, dropout, is_last=is_last)
nb_conv += 2
# Add Level 2 shortcut
x = Add()([x, conv3_level2_shortcut])
# Add Level 1 shortcut
x = Add()([x, conv0_level1_shortcut])
x = Activation('relu')(x)
x = AveragePooling2D((8, 8))(x)
x = Flatten()(x)
x = Dense(nb_classes, activation='softmax')(x)
model = Model(ip, x)
if verbose: print("Residual-in-Residual-Network-%d created." % (nb_conv))
return model
if __name__ == '__main__':
model = create_residual_of_residual((3, 32, 32), 10, N=2)
conv_count = 0
for layer in model.layers:
if 'conv' in layer.name:
conv_count += 1
print('Number of convolution layers : ', conv_count)