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import keras
from keras.layers import Input, Dense, BatchNormalization, Flatten, concatenate, Conv3D, MaxPooling3D, Dropout, Lambda, AveragePooling3D, ZeroPadding3D, merge
from keras.layers.core import Activation
def (inputs, k_num, reduction = 0.5, dropout_rate=None):
l = BatchNormalization()(inputs)
l = Activation('relu')(l)
l = Conv3D(int(k_num * reduction), kernel_size = (1, 1, 1), strides= (1, 1, 1), padding='valid')(l)
if dropout_rate is not None:
l = Dropout(dropout_rate)(l)
l = AveragePooling3D(pool_size = (2, 2, 2), strides = (2, 2, 2))(l)
return l
def DenseBlock_3D(inputs, layer_num = 6, k_num = 32, dropout_rate = None, kernel_szie = (3, 3, 3), strides = (1, 1, 1)):
concat_l = inputs
for layer_idx in range(0, layer_num):
l = BatchNormalization()(concat_l)
l = Activation('relu')(l)
l = Conv3D(k_num * 4, kernel_size = (1, 1, 1), strides = (1, 1, 1), padding='valid')(l)
if dropout_rate is not None:
l = Dropout(dropout_rate)(l)
l = BatchNormalization()(l)
l = Activation('relu')(l)
l = Conv3D(k_num, kernel_size = kernel_szie, strides = strides, padding = 'same')(l)
concat_l = concatenate([l, concat_l], axis=-1)
return l
def DenseNet_3D(inputs, block_layer_nums = [6, 12, 18, 12], k_init = 32, k_inc = 32, final_pooling_size = (6, 6, 6), dropout_rate = None):
l = ZeroPadding3D((2, 2, 2))(inputs)
l = Conv3D(k_init, kernel_size = (5, 5, 5), strides = (1, 1, 1), padding='valid')(l)
l = ZeroPadding3D((1, 1, 1))(l)
l = MaxPooling3D(pool_size = (3, 3, 3), strides = (2, 2, 2))(l)
k_num = k_init
down_sample = False
for block_layer_num in block_layer_nums:
if down_sample:
l = TransLayer_3D(l, k_num=k_num, reduction=0.5)
down_sample = True
l = DenseBlock_3D(l, layer_num = block_layer_num, k_num = k_num, dropout_rate = dropout_rate)
k_num += k_inc
l = AveragePooling3D(pool_size = final_pooling_size, strides = final_pooling_size)(l)
l = Flatten()(l)
return l
def ResBlock_3D(inputs, filer_num, layer_num = 2, short_cut_turn = False, down_sample = False):
l = short_cut = inputs
strides = (1, 1, 1)
padding = 'same'
if down_sample:
strides = (2, 2, 2)
padding = 'valid'
if short_cut_turn == True:
if down_sample:
short_cut = ZeroPadding3D((1, 1, 1))(short_cut)
short_cut = Conv3D(filer_num, kernel_size = (3, 3, 3), strides = strides, padding = padding)(short_cut)
for layer_idx in range(layer_num - 1):
if down_sample:
l = ZeroPadding3D((1, 1, 1))(l)
l = Conv3D(filer_num, kernel_size = (3, 3, 3), strides = strides, padding = padding)(l)
if down_sample:
down_sample = False
strides = (1, 1, 1)
padding = 'same'
l = BatchNormalization()(l)
l = Activation('relu')(l)
l = Conv3D(filer_num, kernel_size=(3, 3, 3), strides=strides, padding=padding)(l)
l = merge([l, short_cut], mode = 'sum')
l = BatchNormalization()(l)
l = Activation('relu')(l)
return l
def ResNet_3D(inputs, k_init, k_nums = [32, 64, 128], res_block_nums = [4, 8, 16], final_pooling_size = (6, 6, 6)):
l = ZeroPadding3D((2, 2, 2))(inputs)
l = Conv3D(k_init, (5, 5, 5), strides = (1, 1, 1), padding = 'valid')(l)
l = ZeroPadding3D((1, 1, 1))(l)
l = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2))(l)
l = BatchNormalization()(l)
l = Activation('relu')(l)
assert(len(k_nums) == len(res_block_nums))
short_cut_turn = True
down_sample = False
for res_block_idx in range(len(k_nums)):
for _ in range(res_block_nums[res_block_idx]):
l = ResBlock_3D(l, k_nums[res_block_idx], 2, short_cut_turn, down_sample = down_sample)
down_sample = False
short_cut_turn = False
down_sample = True
short_cut_turn = True
l = AveragePooling3D(pool_size = final_pooling_size, strides = final_pooling_size)(l)
l = Flatten()(l)
return l
def InceptionResBlock_3D(inputs, k_num):
l1 = Conv3D(k_num, (1, 1, 1), strides=(1, 1, 1), padding="same")(inputs)
l2 = Conv3D(k_num // 4, (1, 1, 1), strides=(1, 1, 1), padding="same")(inputs)
l2 = BatchNormalization()(l2)
l2 = Activation('relu')(l2)
l2 = Conv3D(k_num, (3, 3, 3), strides=(1, 1, 1), padding="same")(l2)
l2 = BatchNormalization()(l2)
l2 = Activation('relu')(l2)
l2 = Conv3D(k_num, (3, 3, 3), strides=(1, 1, 1), padding="same")(l2)
l2 = BatchNormalization()(l2)
l2 = Activation('relu')(l2)
l2 = Conv3D(k_num, (3, 3, 3), strides=(1, 1, 1), padding="same")(l2)
l3 = Conv3D(k_num // 4, (1, 1, 1), strides=(1, 1, 1), padding="same")(inputs)
l3 = BatchNormalization()(l3)
l3 = Activation('relu')(l3)
l3 = Conv3D(k_num, (5, 5, 5), strides=(1, 1, 1), padding="same")(l3)
l3 = BatchNormalization()(l3)
l3 = Activation('relu')(l3)
l3 = Conv3D(k_num, (5, 5, 5), strides=(1, 1, 1), padding="same")(l3)
l = concatenate([l1, l2, l3])
l = BatchNormalization()(l)
l = Activation('relu')(l)
l = Conv3D(k_num, (1, 1, 1), strides = (1, 1, 1), padding = 'same')(l)
l = merge([l, inputs], mode = 'sum')
l = BatchNormalization()(l)
l = Activation('relu')(l)
return l
def InceptionResNet_3D(inputs, k_init = 32, k_nums = [64, 128, 256], final_pooling_size = [6, 6, 6]):
l = ZeroPadding3D((2, 2, 2))(inputs)
l = Conv3D(k_init, (5, 5, 5), strides = (1, 1, 1), padding = 'valid')(l)
l = ZeroPadding3D((1, 1, 1))(l)
l = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2))(l)
l = BatchNormalization()(l)
l = Activation('relu')(l)
down_sample = False
for k_num in k_nums:
if down_sample:
l1 = ZeroPadding3D((2, 2, 2))(l)
l1 = Conv3D(k_num, (5, 5, 5), strides = (2, 2, 2), padding = 'valid')(l1)
l2 = ZeroPadding3D((1, 1, 1))(l)
l2 = Conv3D(k_num, (3, 3, 3), strides = (2, 2, 2), padding = 'valid')(l2)
l2 = BatchNormalization()(l2)
l2 = Activation('relu')(l2)
l2 = Conv3D(k_num, (3, 3, 3), padding='same')(l2)
l3 = MaxPooling3D((2, 2, 2), strides = (2, 2, 2))(l)
l = concatenate([l1, l2, l3])
l = BatchNormalization()(l)
l = Activation('relu')(l)
l = Conv3D(k_num, (1, 1, 1), strides = (1, 1, 1), padding = 'valid')(l)
l = BatchNormalization()(l)
l = Activation('relu')(l)
down_sample = True
l = InceptionResBlock_3D(l, k_num)
l = AveragePooling3D(final_pooling_size, final_pooling_size)(l)
l = Flatten()(l)
return l
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