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densenet.py
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densenet.py
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from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.layers import Input, Concatenate
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
import keras.backend as K
def conv_factory(x, concat_axis, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 3x3Conv2D, optional dropout
:parameter x: Input keras network
:parameter concat_axis: int -- index of contatenate axis
:parameter nb_filter: int -- number of filters
:parameter dropout_rate: int -- dropout rate
:parameter weight_decay: int -- weight decay factor
:returns: keras network with b_norm, relu and Conv2D added
:return type: keras network
"""
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (1, 1), kernel_initializer="he_uniform", padding="same", use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (3, 3), kernel_initializer="he_uniform", padding="same", use_bias=False, kernel_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition(x, concat_axis, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D
:parameter x: keras model
:parameter concat_axis: int -- index of contatenate axis
:parameter nb_filter: int -- number of filters
:parameter dropout_rate: int -- dropout rate
:parameter weight_decay: int -- weight decay factor
:returns: model
:return type: keras model, after applying batch_norm, relu-conv, dropout, maxpool
"""
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (1, 1), kernel_initializer="he_uniform", padding="same", use_bias=False, kernel_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
return x
def denseblock(x, concat_axis, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1E-4):
"""
Build a denseblock where the output of each conv_factory is fed to subsequent ones
:parameter x: keras model
:parameter concat_axis: int -- index of contatenate axis
:parameter nb_layers: int -- the number of layers of conv_factory to append to the model.
:parameter nb_filter: int -- number of filters
:parameter dropout_rate: int -- dropout rate
:parameter weight_decay: int -- weight decay factor
:returns: keras model with nb_layers of conv_factory appended
:return type: keras model
"""
list_feat = [x]
for i in range(nb_layers):
x = conv_factory(x, concat_axis, growth_rate,
dropout_rate, weight_decay)
list_feat.append(x)
x = Concatenate(axis=concat_axis)(list_feat)
nb_filter += growth_rate
#print (nb_filter)
return x, nb_filter
def denseblock_altern(x, concat_axis, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each conv_factory is fed to subsequent ones. (Alternative of denseblock)
:parameter x: keras model
:parameter concat_axis: int -- index of contatenate axis
:parameter nb_layers: int -- the number of layers of conv_factory to append to the model.
:parameter nb_filter: int -- number of filters
:parameter dropout_rate: int -- dropout rate
:parameter weight_decay: int -- weight decay factor
:returns: keras model with nb_layers of conv_factory appended
:return type: keras model
* The main difference between this implementation and the implementation
above is that the one above
"""
for i in range(nb_layers):
merge_tensor = conv_factory(x, concat_axis, growth_rate, dropout_rate, weight_decay)
x = Concatenate(axis=concat_axis)([merge_tensor, x])
nb_filter += growth_rate
return x, nb_filter
def DenseNet(nb_classes, img_dim, depth, nb_dense_block, growth_rate, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""
Build the DenseNet model
:parameter nb_classes: int -- number of classes
:parameter img_dim: tuple -- (channels, rows, columns)
:parameter depth: int -- how many layers
:parameter nb_dense_block: int -- number of dense blocks to add to end
:parameter growth_rate: int -- number of filters to add
:parameter nb_filter: int -- number of filters
:parameter dropout_rate: float -- dropout rate
:parameter weight_decay: float -- weight decay
:returns: keras model with nb_layers of conv_factory appended
:return type: keras model
"""
if K.image_dim_ordering() == "th":
concat_axis = 1
elif K.image_dim_ordering() == "tf":
concat_axis = -1
model_input = Input(shape=img_dim)
assert (depth - 4) % 3 == 0, "Depth must be 3 N + 4"
# layers in each dense block
nb_layers = int((depth - 4) / 3)
# Initial convolution
x = Conv2D(nb_filter, (7, 7), strides=(2, 2), kernel_initializer="he_uniform", padding="same", name="initial_conv2D", use_bias=False, kernel_regularizer=l2(weight_decay))(model_input)
# Add dense blocks
nb_layers1 = [6,12,32,32,48,32,48,64,32] #3*3 convolutional layer of each denseblock ，
for block_idx in range(nb_dense_block - 1):
x, nb_filter = denseblock(x, concat_axis, nb_layers1[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
# add transition
x = transition(x, concat_axis,nb_filter, dropout_rate=dropout_rate, weight_decay=weight_decay)
# The last denseblock does not have a transition
x, nb_filter = denseblock(x, concat_axis, nb_layers1[nb_dense_block-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)
x = Activation('relu')(x)
x = GlobalAveragePooling2D(data_format=K.image_data_format())(x)
x = Dense(nb_classes, activation='sigmoid', kernel_regularizer=l2(weight_decay), bias_regularizer=l2(weight_decay))(x)
densenet = Model(inputs=[model_input], outputs=[x], name="DenseNet")
return densenet