def build_model(input_shape):
xin = Input(input_shape)
#shift the below down by one
x1 = conv_block(xin,8,activation='relu')
x1_ident = AveragePooling3D()(xin)
x1_merged = merge([x1, x1_ident],mode='concat', concat_axis=1)
x2_1 = conv_block(x1_merged,24,activation='relu') #outputs 37 ch
x2_ident = AveragePooling3D()(x1_ident)
x2_merged = merge([x2_1,x2_ident],mode='concat', concat_axis=1)
#by branching we reduce the #params
x3_ident = AveragePooling3D()(x2_ident)
x3_malig = conv_block(x2_merged,48,activation='relu') #outputs 25 + 16 ch = 41
x3_malig_merged = merge([x3_malig,x3_ident],mode='concat', concat_axis=1)
x4_ident = AveragePooling3D()(x3_ident)
x4_malig = conv_block(x3_malig_merged,64,activation='relu') #outputs 25 + 16 ch = 41
x4_merged = merge([x4_malig,x4_ident],mode='concat', concat_axis=1)
x5_malig = conv_block(x4_merged,64) #outputs 25 + 16 ch = 41
xpool_malig = BatchNormalization(momentum=0.995)(GlobalMaxPooling3D()(x5_malig))
xout_malig = Dense(1, name='o_mal', activation='relu')(xpool_malig) #relu output
x5_diam = conv_block(x4_merged,64) #outputs 25 + 16 ch = 41
xpool_diam = BatchNormalization(momentum=0.995)(GlobalMaxPooling3D()(x5_diam))
xout_diam = Dense(1, name='o_diam', activation='relu')(xpool_diam) #relu output
x5_lob = conv_block(x4_merged,64) #outputs 25 + 16 ch = 41
xpool_lob = BatchNormalization(momentum=0.995)(GlobalMaxPooling3D()(x5_lob))
xout_lob = Dense(1, name='o_lob', activation='relu')(xpool_lob) #relu output
x5_spic = conv_block(x4_merged,64) #outputs 25 + 16 ch = 41
xpool_spic = BatchNormalization(momentum=0.995)(GlobalMaxPooling3D()(x5_spic))
xout_spic = Dense(1, name='o_spic', activation='relu')(xpool_spic) #relu output
model = Model(input=xin,output=[xout_diam, xout_lob, xout_spic, xout_malig])
if input_shape[1] == 32:
lr_start = .01
elif input_shape[1] == 64:
lr_start = .003
elif input_shape[1] == 128:
lr_start = .002
# elif input_shape[1] == 96:
# lr_start = 5e-4
opt = Nadam(lr_start,clipvalue=1.0)
print 'compiling model'
model.compile(optimizer=opt,loss='mse',loss_weights={'o_diam':0.06, 'o_lob':0.5, 'o_spic':0.5, 'o_mal':1.0})
return model
def build_model(input_shape):
xin = Input(input_shape)
#shift the below down by one
x1 = conv_block(xin, 8, activation='relu')
x1_ident = AveragePooling3D()(xin)
x1_merged = merge([x1, x1_ident], mode='concat', concat_axis=1)
x2_1 = conv_block(x1_merged, 24, activation='relu') #outputs 37 ch
x2_ident = AveragePooling3D()(x1_ident)
x2_merged = merge([x2_1, x2_ident], mode='concat', concat_axis=1)
#by branching we reduce the #params
x3_ident = AveragePooling3D()(x2_ident)
x3_malig = conv_block(x2_merged, 36,
activation='relu') #outputs 25 + 16 ch = 41
x3_malig_merged = merge([x3_malig, x3_ident], mode='concat', concat_axis=1)
x4_ident = AveragePooling3D()(x3_ident)
x4_malig = conv_block(x3_malig_merged, 48,
activation='relu') #outputs 25 + 16 ch = 41
x4_malig_merged = merge([x4_malig, x4_ident], mode='concat', concat_axis=1)
x5_malig = conv_block(x4_malig_merged, 64) #outputs 25 + 16 ch = 41
xpool_malig = BatchNormalization(momentum=0.995)(
GlobalMaxPooling3D()(x5_malig))
xout_malig = Dense(1, name='o_mal',
activation='sigmoid')(xpool_malig) #sigmoid output
model = Model(input=xin, output=xout_malig)
if input_shape[1] == 32:
lr_start = .01
elif input_shape[1] == 64:
lr_start = .003
elif input_shape[1] == 128:
lr_start = .002
# elif input_shape[1] == 96:
# lr_start = 5e-4
opt = Nadam(lr_start, clipvalue=1.0)
print 'compiling model'
model.compile(optimizer=opt, loss='mae')
return model
def Attention_block(input_tensor,
spatial_attention=True,
temporal_attention=True):
"""
@param input_tensor: 输入张量
@param spatial_attention: flag to decide to allow spatial_attention
@param temporal_attention: flag to decide to allow temporal_attention
@return: tensor after attention
"""
tem = input_tensor
# 将任意表达式封装为一个layer对象
x = Lambda(channel_wise_mean)(input_tensor)
x = keras.layers.Reshape([
K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2],
K.int_shape(input_tensor)[3], 1
])(x)
nbSpatial = K.int_shape(input_tensor)[1] * K.int_shape(input_tensor)[2]
nbTemporal = K.int_shape(input_tensor)[-2]
if spatial_attention:
spatial = AveragePooling3D(
pool_size=[1, 1, K.int_shape(input_tensor)[-2]])(x)
spatial = keras.layers.Flatten()(spatial)
spatial = Dense(nbSpatial)(spatial)
spatial = Activation('sigmoid')(spatial)
spatial = keras.layers.Reshape(
[K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2], 1, 1])(spatial)
tem = keras.layers.multiply([input_tensor, spatial])
if temporal_attention:
temporal = AveragePooling3D(pool_size=[
K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2], 1
])(x)
temporal = keras.layers.Flatten()(temporal)
temporal = Dense(nbTemporal)(temporal)
temporal = Activation('sigmoid')(temporal)
temporal = keras.layers.Reshape(
[1, 1, K.int_shape(input_tensor)[-2], 1])(temporal)
tem = keras.layers.multiply([temporal, tem])
return tem
def down_stage(ip,
nb_layers,
nb_filter,
growth_rate,
dropout_rate,
weight_decay,
compression,
pooling=True):
x0, nb_filter = __dense_block(ip,
nb_layers,
nb_filter,
growth_rate,
bottleneck=True,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
print('x:', K.int_shape(x0))
print(nb_filter)
x1 = transition_block(x0, nb_filter, weight_decay=weight_decay)
x = transition_block(ip, nb_filter, weight_decay=weight_decay)
# addx=Apply_Attention(x1,x)
addx = add([x, x1])
if pooling:
out = AveragePooling3D(strides=(2, 2, 2))(addx)
return addx, out
return addx
def down_stage(ip,
nb_layers,
nb_filter,
growth_rate,
dropout_rate,
weight_decay,
compression,
train_flage,
name_flage,
pooling=True):
x0, nb_filter = __dense_block(ip,
nb_layers,
nb_filter,
growth_rate,
bottleneck=True,
dropout_rate=dropout_rate,
weight_decay=weight_decay,
train_flage=train_flage,
name_flage=name_flage + '_DB_')
x1 = transition_block(x0,
nb_filter,
weight_decay=weight_decay,
train_flage=train_flage,
name_flage=name_flage + '_TB0_')
x = transition_block(ip,
nb_filter,
weight_decay=weight_decay,
train_flage=train_flage,
name_flage=name_flage + '_TB1_')
addx = add([x, x1], name=name_flage + 'ADD')
if pooling:
out = AveragePooling3D(strides=(2, 2, 2))(addx)
return addx, out
return addx
def Gen_Dis_model(trable_flage=True,reduction=0.5, dropout_rate=0.3, weight_decay=5e-4):
if K.image_data_format() == 'channels_last':
img_input = Input(shape=(img_rows,img_cols,chan,1))
concat_axis=-1
else:
img_input = Input(shape=(1,chan, img_rows, img_cols))
concat_axis=1
if reduction != 0.0:
assert reduction <= 1.0 and reduction > 0.0, 'reduction value must lie between 0.0 and 1.0'
# compute compression factor
compression = 1.0 - reduction
nb_layers=[4,8,16,8,4,2]
growth_rate=32
# Initial convolution
#stage1
x1 = Conv3D(64, (3,3,3),strides=(1,1,1),kernel_initializer='he_normal',padding='same',
use_bias=False, kernel_regularizer=l2(weight_decay),trainable=True,name='x1')(img_input)
x = BatchNormalization(axis=concat_axis,trainable=True,name='x1_BN')(x1)
x = Activation('relu')(x)
x = AveragePooling3D((2,2,2))(x)
#stage1
s1_x0,s1_x = down_stage(x,nb_layers[0],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s1')
#stage2
s2_x0,s2_x = down_stage(s1_x,nb_layers[1],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s2')
#stage3
s3_x0 = down_stage(s2_x,nb_layers[2],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s3',pooling=False)
#stage4
D1 = Decon_stage(s3_x0,256,kernel_size=(3,3,3),strides=(2,2,2),weight_decay=weight_decay,train_flage=True,name_flage='D1')
con1 = add([D1,s2_x0])
s4_x = up_stage(con1,nb_layers[3],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s4')
#stage5
D2 = Decon_stage(s4_x,128,kernel_size=(3,3,3),strides=(2,2,2),weight_decay=weight_decay,train_flage=True,name_flage='D2')
con2 =add([D2,s1_x0])
s5_x = up_stage(con2,nb_layers[4],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s5')
#stage6
D3 = Decon_stage(s5_x,64,kernel_size=(3,3,3),strides=(2,2,2),weight_decay=weight_decay,train_flage=True,name_flage='D3')
con3 = add([D3,x1])
s6_x = up_stage(con3,nb_layers[5],0,growth_rate,dropout_rate,weight_decay,compression,train_flage=True,name_flage='s6')
main_out = output(s6_x,weight_decay,train_flage=True,name_flage='out')
Discriminator_model.trainable=False
GAN_loss = Discriminator_model()([s4_x,s5_x,s6_x])
model = Model(img_input, [main_out,main_out,GAN_loss])
sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss=[Dist_Loss,'binary_crossentropy','binary_crossentropy' ], loss_weights=[0.1,1.0,1.0], metrics=[dice_coef] )#
return model
def transition_block(input, nb_filter, dropout_rate=None, weight_decay=1E-4):
''' Apply BatchNorm, Relu 1x1, Conv2D, optional dropout and Maxpooling2D
Args:
input: keras tensor
nb_filter: number of filters
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = Convolution3D(nb_filter, (1, 1, 1),
kernel_initializer="he_uniform",
padding="same",
use_bias=False,
kernel_regularizer=l2(weight_decay))(input)
if dropout_rate is not None:
x = Dropout(dropout_rate)(x)
x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2))(x)
x = BatchNormalization(axis=concat_axis,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
return x
def ASPP(x, filters):
shape = x.shape
y1 = AveragePooling3D(pool_size=(shape[1], shape[2], shape[3]))(x)
y1 = Conv3D(filters/2, 1, padding="same")(y1)
y1 = BatchNormalization()(y1)
y1 = Activation("relu")(y1)
y1 = UpSampling3D((shape[1], shape[2], shape[3]))(y1)
y2 = Conv3D(filters/2, 1, dilation_rate=1, padding="same", use_bias=False)(x)
y2 = BatchNormalization()(y2)
y2 = Activation("relu")(y2)
y3 = Conv3D(filters/2, 3, dilation_rate=2, padding="same", use_bias=False)(x)
y3 = BatchNormalization()(y3)
y3 = Activation("relu")(y3)
y4 = Conv3D(filters/2, 3, dilation_rate=4, padding="same", use_bias=False)(x)
y4 = BatchNormalization()(y4)
y4 = Activation("relu")(y4)
y5 = Conv3D(filters/2, 3, dilation_rate=8, padding="same", use_bias=False)(x)
y5 = BatchNormalization()(y5)
y5 = Activation("relu")(y5)
y = Concatenate()([y1, y2, y3, y4, y5])
y = Conv3D(filters, 1, dilation_rate=1, padding="same", use_bias=False)(y)
y = BatchNormalization()(y)
y = Activation("relu")(y)
return y
def CFF(input_list, input_size, filters, i):
out_shape = input_size/pow(2,i)
y = tf.zeros_like(input_list[i-1])
for j,x in enumerate(input_list):
if j < i-1:
down_factor = int((input_size/pow(2,j+1)) / out_shape)
x = AveragePooling3D((down_factor, down_factor, down_factor))(x)
x = Conv3D(filters, (1, 1, 1), padding='same')(x)
sigm = Activation('sigmoid')(x)
x = Multiply()([x, sigm])
y = Add()([y, x])
if j > i-1:
up_factor = int(out_shape / (input_size/pow(2,j+1)))
x = Conv3D(filters, (1, 1, 1), padding='same')(x)
x = UpSampling3D((up_factor, up_factor, up_factor))(x)
sigm = Activation('sigmoid')(x)
x = Multiply()([x, sigm])
y = Add()([y,x])
x_i = input_list[i-1]
x_i_sigm = Activation('sigmoid')(x_i)
x_i_sigm = -1 * x_i_sigm + 1
out = Multiply()([x_i_sigm, y])
out = Add()([out, x_i])
return out
def build_model(input_shape):
xin = Input(input_shape)
x1 = conv_block(xin,8,activation='crelu')
x1_ident = AveragePooling3D()(xin)
x1_merged = concatenate([x1, x1_ident], axis=1)
x2_1 = conv_block(x1_merged,24,activation='crelu',init='orthogonal')
x2_ident = AveragePooling3D()(x1_ident)
x2_merged = concatenate([x2_1,x2_ident], axis=1)
#by branching we reduce the #params
x3_1 = conv_block(x2_merged,36,activation='crelu',init='orthogonal')
x3_ident = AveragePooling3D()(x2_ident)
x3_merged = concatenate([x3_1,x3_ident], axis=1)
x4_1 = conv_block(x3_merged,36,activation='crelu',init='orthogonal')
x4_ident = AveragePooling3D()(x3_ident)
x4_merged = concatenate([x4_1,x4_ident], axis=1)
x5_1 = conv_block(x4_merged,64,pool=False,init='orthogonal')
xpool = BatchNormalization()(GlobalMaxPooling3D()(x5_1))
xout = dense_branch(xpool,outsize=1,activation='sigmoid')
model = Model(input=xin,output=xout)
if input_shape[1] == 32 :
lr_start = 1e-5
elif input_shape[1] == 64:
lr_start = 1e-5
elif input_shape[1] == 128:
lr_start = 1e-4
elif input_shape[1] == 96:
lr_start = 5e-4
elif input_shape[1] == 16:
lr_start = 1e-6
opt = Nadam(lr_start,clipvalue=1.0)
print('compiling model')
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
return model
def SqueezeModel2(input_tensor):
droprate = 0.2
x = Conv3D(filters=96,
kernel_size=(1, 1, 1),
strides = (1, 1, 1),
padding='same',
activation='relu',
kernel_initializer=glorot_uniform(seed=1),
bias_initializer='zeros', name="conv1")(input_tensor)
x = Conv3D(filters=16, kernel_size=(1, 1, 1), kernel_initializer=glorot_uniform(seed=1), activation='relu', name="fire2_squeeze")(x)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(64, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire2_expand1")(x)
expand2 = Conv3D(64, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire2_expand2")(x)
merge1 = concatenate([expand1, expand2], axis=4, name="merge_1")
x = Conv3D(16, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire3_squeeze")(merge1)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(64, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire3_expand1")(x)
expand2 = Conv3D(64, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire3_expand2")(x)
merge2 = concatenate([expand1, expand2], axis=4, name="merge_2")
x = Conv3D(32, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire4_squeeze")(merge2)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(128, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire4_expand1")(x)
expand2 = Conv3D(128, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire4_expand2")(x)
merge3 = concatenate([expand1, expand2], axis=4, name="merge_3")
maxpool4 = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), name="maxpool_4")(merge3)
x = Conv3D(filters=32, kernel_size=(1, 1, 1), activation='relu', name="fire5_squeeze")(maxpool4)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(128, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire5_expand1")(x)
expand2 = Conv3D(128, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire5_expand2")(x)
merge4 = concatenate([expand1, expand2], axis=4, name="merge_4")
x = Conv3D(48, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire6_squeeze")(merge4)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(192, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire6_expand1")(x)
expand2 = Conv3D(192, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire6_expand2")(x)
merge5 = concatenate([expand1, expand2], axis=4, name="merge_5")
x = Conv3D(48, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire7_squeeze")(merge5)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(192, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire7_expand1")(x)
expand2 = Conv3D(192, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire7_expand2")(x)
merge6 = concatenate([expand1, expand2], axis=4, name="merge_6")
x = Conv3D(64, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire8_squeeze")(merge6)
x = BatchNormalization()(x)
x = Dropout(droprate)
expand1 = Conv3D(256, (1, 1, 1), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire8_expand1")(x)
expand2 = Conv3D(256, (3, 3, 3), kernel_initializer=glorot_uniform(seed=1), padding='same', activation='relu', name="fire8_expand2")(x)
merge7 = concatenate([expand1, expand2], axis=4, name="merge_7")
avgpool = AveragePooling3D(pool_size=(3, 3, 3), padding='same', name="avg8")(merge7)
flatten = Flatten(name="flatten")(avgpool)
output = Dense(1, activation='linear', kernel_initializer=glorot_uniform(seed=1))(flatten)
return output
def PEE(x, filters):
if filters > 30:
pool_size_1 = (3, 3, 3)
pool_size_2 = (5, 5, 5)
else:
pool_size_1 = (5, 5, 5)
pool_size_2 = (7, 7, 7)
x = Conv3D(filters/2, (1, 1, 1), padding='same')(x)
x_1 = AveragePooling3D(pool_size=pool_size_1, strides = (1, 1, 1), padding='same')(x)
x_2 = AveragePooling3D(pool_size=pool_size_2, strides = (1, 1, 1), padding='same')(x)
x_11 = Subtract()([x, x_1])
x_22 = Subtract()([x, x_2])
x = Concatenate()([x, x_11, x_22])
x = Conv3D(filters, (1, 1, 1), padding='same')(x)
return x
def Attention_block(input_tensor,
spatial_attention=True,
temporal_attention=True):
tem = input_tensor
x = Lambda(channel_wise_mean)(input_tensor)
x = keras.layers.Reshape([
K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2],
K.int_shape(input_tensor)[3], 1
])(x)
nbSpatial = K.int_shape(input_tensor)[1] * K.int_shape(input_tensor)[2]
nbTemporal = K.int_shape(input_tensor)[-2]
if spatial_attention:
spatial = AveragePooling3D(
pool_size=[1, 1, K.int_shape(input_tensor)[-2]])(x)
spatial = keras.layers.Flatten()(spatial)
spatial = Dense(nbSpatial)(spatial)
spatial = Activation('sigmoid')(spatial)
spatial = keras.layers.Reshape(
[K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2], 1, 1])(spatial)
tem = keras.layers.multiply([input_tensor, spatial])
if temporal_attention:
temporal = AveragePooling3D(pool_size=[
K.int_shape(input_tensor)[1],
K.int_shape(input_tensor)[2], 1
])(x)
temporal = keras.layers.Flatten()(temporal)
temporal = Dense(nbTemporal)(temporal)
temporal = Activation('sigmoid')(temporal)
temporal = keras.layers.Reshape(
[1, 1, K.int_shape(input_tensor)[-2], 1])(temporal)
tem = keras.layers.multiply([temporal, tem])
return tem
def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
x = Activation('relu')(x)
x = Conv3D(int(nb_filter * compression), (1, 1, 1),
kernel_initializer='he_normal',
padding='same',
use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2))(x)
return x
def DenseNetTransit(x, rate=1, name=None):
if rate != 1:
out_features = x.get_shape().as_list()[-1] * rate
x = BatchNormalization(center=True, scale=True, name=name + '_bn')(x)
x = Activation('relu', name=name + '_relu')(x)
x = Conv3D(filters=out_features,
kernel_size=1,
strides=1,
padding='same',
kernel_initializer='he_normal',
use_bias=False,
name=name + '_conv')(x)
x = AveragePooling3D(pool_size=2, strides=2, padding='same')(x)
return x
def transition_block(x,
stage,
nb_filter,
compression=1.0,
dropout_rate=None,
weight_decay=1E-4):
''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout
# Arguments
x: input tensor
stage: index for dense block
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
'''
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_blk'
relu_name_base = 'relu' + str(stage) + '_blk'
pool_name_base = 'pool' + str(stage)
## 1x1x1 convolution
x = BatchNormalization(epsilon=eps,
axis=concat_axis,
name=conv_name_base + '_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base + '_scale')(x)
x = Activation('relu', name=relu_name_base)(x)
x = Convolution3D(int(nb_filter * compression),
1,
1,
1,
name=conv_name_base,
bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
## 2x2x2 avg pooling
x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2), name=pool_name_base)(x)
return x
def conv3d(layer_input, filters, f_size=3, bn=True):
"""Layers used during downsampling"""
if self.settings['POOLING'] == "MAX":
d = Conv3D(filters, kernel_size=f_size, strides=1, padding='same')(layer_input)
d = MaxPooling3D(padding='same', data_format="channels_last")(d)
elif self.settings['POOLING'] == "AVERAGE":
d = Conv3D(filters, kernel_size=f_size, strides=1, padding='same')(layer_input)
d = AveragePooling3D(padding='same', data_format="channels_last")(d)
elif self.settings['POOLING'] == "NONE":
d = Conv3D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
# GANHACKS: add dropout with specified value
if self.settings['GANHACKS']:
d = Dropout(rate=self.settings['DROPOUT'])(d)
if bn and self.settings['BATCH_NORMALIZATION']:
d = BatchNormalization(momentum=0.8)(d)
# GANHACKS: adding gaussian noise to every layer of G (Zhao et. al. EBGAN)
if self.settings['GANHACKS']:
d = GaussianNoise(stddev=self.settings['GAUSSIAN_NOISE_TO_G'])(d)
print('downsampling:\t\t\t', d.shape)
return d
def dense_model():
model = Sequential()
model.add(Reshape((100, 100, 4, 1), input_shape=(100, 100, 4)))
model.add(
Lambda(
lambda x: 2 * x - 1.,
batch_input_shape=(1, 100, 100, 4), # 100by100by2
output_shape=(100, 100, 4, 1))) # 100by100by2
model.add(
Convolution3D(16,
kernel_size=(8, 8, 2),
subsample=(4, 4, 1),
border_mode="valid",
bias_initializer="random_uniform"))
model.add(ELU())
model.add(AveragePooling3D(pool_size=(2, 2, 1)))
model.add(
Convolution3D(16,
kernel_size=(4, 4, 2),
subsample=(2, 2, 1),
border_mode="valid",
bias_initializer="random_uniform"))
model.add(ELU())
model.add(
Convolution3D(24,
kernel_size=(3, 3, 2),
subsample=(1, 1, 1),
border_mode="valid",
bias_initializer="random_uniform"))
model.add(ELU())
model.add(Flatten())
model.add(Dense(256, bias_initializer="random_uniform"))
model.add(ELU())
model.add(Dense(32, activation="relu", bias_initializer="random_uniform"))
model.add(Dense(2, bias_initializer="random_uniform"))
sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
model.compile(optimizer=sgd, loss="mse")
return model
def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
"""
@param ip:keras tensor
@param nb_filter:number of filters
@param compression:
压缩系数小于1: DenseNet-C 压缩系数小于1+bottleneck :DenseNet-BC
caculated as 1 - reduction. Reduces the number of features maps in the transition block
@param weight_decay:weight decay factor
@return x : keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
"""
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
x = Activation('relu')(x)
x = Conv3D(int(nb_filter * compression), (1, 1, 1),
kernel_initializer='he_normal',
padding='same',
use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2))(x)
return x
def __transition_block(ip,
nb_filter,
compression=1.0,
dropout_rate=None,
weight_decay=1E-4):
''' Apply BatchNorm, Relu 1x1, Conv2D, optional compression, dropout and Maxpooling2D
Args:
ip: keras tensor
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps
in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
'''
concat_axis = 1 if K.image_dim_ordering() == "th" else -1
x = BatchNormalization(mode=0,
axis=concat_axis,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(ip)
x = Activation('relu')(x)
x = Convolution3D(int(nb_filter * compression),
1,
1,
1,
init="he_uniform",
border_mode="same",
bias=False,
W_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2))(x)
return x
def build_model(input_shape):
xin = Input(input_shape)
#shift the below down by one
x1 = conv_block(xin, 8, activation='crelu') #outputs 13 ch
x1_ident = AveragePooling3D()(xin)
x1_merged = merge([x1, x1_ident], mode='concat', concat_axis=1)
x2_1 = conv_block(x1_merged,
24,
activation='crelu',
init=looks_linear_init) #outputs 37 ch
x2_ident = AveragePooling3D()(x1_ident)
x2_merged = merge([x2_1, x2_ident], mode='concat', concat_axis=1)
#by branching we reduce the #params
x3_ident = AveragePooling3D()(x2_ident)
x3_diam = conv_block(x2_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x3_lob = conv_block(x2_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x3_spic = conv_block(x2_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x3_malig = conv_block(x2_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x3_diam_merged = merge([x3_diam, x3_ident], mode='concat', concat_axis=1)
x3_lob_merged = merge([x3_lob, x3_ident], mode='concat', concat_axis=1)
x3_spic_merged = merge([x3_spic, x3_ident], mode='concat', concat_axis=1)
x3_malig_merged = merge([x3_malig, x3_ident], mode='concat', concat_axis=1)
x4_ident = AveragePooling3D()(x3_ident)
x4_diam = conv_block(x3_diam_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x4_lob = conv_block(x3_lob_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x4_spic = conv_block(x3_spic_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x4_malig = conv_block(x3_malig_merged,
36,
activation='crelu',
init=looks_linear_init) #outputs 25 + 16 ch = 41
x4_diam_merged = merge([x4_diam, x4_ident], mode='concat', concat_axis=1)
x4_lob_merged = merge([x4_lob, x4_ident], mode='concat', concat_axis=1)
x4_spic_merged = merge([x4_spic, x4_ident], mode='concat', concat_axis=1)
x4_malig_merged = merge([x4_malig, x4_ident], mode='concat', concat_axis=1)
x5_diam = conv_block(x4_diam_merged,
64,
pool=False,
init=looks_linear_init) #outputs 25 + 16 ch = 41
x5_lob = conv_block(x4_lob_merged, 64, pool=False,
init=looks_linear_init) #outputs 25 + 16 ch = 41
x5_spic = conv_block(x4_spic_merged,
64,
pool=False,
init=looks_linear_init) #outputs 25 + 16 ch = 41
x5_malig = conv_block(x4_malig_merged,
64,
pool=False,
init=looks_linear_init) #outputs 25 + 16 ch = 41
xpool_diam = BatchNormalization()(GlobalMaxPooling3D()(x5_diam))
xpool_lob = BatchNormalization()(GlobalMaxPooling3D()(x5_lob))
xpool_spic = BatchNormalization()(GlobalMaxPooling3D()(x5_spic))
xpool_malig = BatchNormalization()(GlobalMaxPooling3D()(x5_malig))
#from here let's branch and predict different things
xout_diam = dense_branch(xpool_diam,
name='o_d',
outsize=1,
activation='relu')
xout_lob = dense_branch(xpool_lob,
name='o_lob',
outsize=1,
activation='sigmoid')
xout_spic = dense_branch(xpool_spic,
name='o_spic',
outsize=1,
activation='sigmoid')
xout_malig = dense_branch(xpool_malig,
name='o_mal',
outsize=1,
activation='sigmoid')
#sphericity
# xout_spher= dense_branch(xpool_norm,name='o_spher',outsize=4,activation='softmax')
# xout_text = dense_branch(xpool_norm,name='o_t',outsize=4,activation='softmax')
#calcification
# xout_calc = dense_branch(xpool_norm,name='o_c',outsize=7,activation='softmax')
model = Model(input=xin,
output=[xout_diam, xout_lob, xout_spic, xout_malig])
if input_shape[1] == 32:
lr_start = .003
elif input_shape[1] == 64:
lr_start = .001
elif input_shape[1] == 128:
lr_start = 1e-4
elif input_shape[1] == 96:
lr_start = 5e-4
opt = Nadam(lr_start, clipvalue=1.0)
print 'compiling model'
model.compile(optimizer=opt,
loss={
'o_d': 'mse',
'o_lob': 'binary_crossentropy',
'o_spic': 'binary_crossentropy',
'o_mal': 'binary_crossentropy'
},
loss_weights={
'o_d': 1.0,
'o_lob': 5.0,
'o_spic': 5.0,
'o_mal': 5.0
})
return model
def build_model(input_shape):
xin = Input(input_shape)
#shift the below down by one
x1 = conv_block(xin, 8, norm=True, drop_rate=0) #outputs 9 ch
x1_ident = AveragePooling3D()(xin)
x1_merged = merge([x1, x1_ident], mode='concat', concat_axis=1)
x2_1 = conv_block(x1_merged, 24, norm=True,
drop_rate=0) #outputs 16+9 ch = 25
x2_ident = AveragePooling3D()(x1_ident)
x2_merged = merge([x2_1, x2_ident], mode='concat', concat_axis=1)
#by branching we reduce the #params
x3_1 = conv_block(x2_merged, 64, norm=True,
drop_rate=0) #outputs 25 + 16 ch = 41
x3_ident = AveragePooling3D()(x2_ident)
x3_merged = merge([x3_1, x3_ident], mode='concat', concat_axis=1)
x4_1 = conv_block(x3_merged, 72, norm=True,
drop_rate=0) #outputs 25 + 16 ch = 41
x4_ident = AveragePooling3D()(x3_ident)
x4_merged = merge([x4_1, x4_ident], mode='concat', concat_axis=1)
x5_1 = conv_block(x4_merged, 72, norm=True, pool=False,
drop_rate=0) #outputs 25 + 16 ch = 41
xpool = GlobalMaxPooling3D()(x5_1)
xpool_norm = BatchNormalization()(xpool)
#xpool_norm = GaussianDropout(.1)(xpool_norm)
#from here let's branch and predict different things
xout_diam = dense_branch(xpool_norm,
name='o_d',
outsize=1,
activation='relu')
#sphericity
# xout_spher= dense_branch(xpool_norm,name='o_spher',outsize=4,activation='softmax')
# xout_text = dense_branch(xpool_norm,name='o_t',outsize=4,activation='softmax')
#calcification
# xout_calc = dense_branch(xpool_norm,name='o_c',outsize=7,activation='softmax')
xout_cad_falsepositive = dense_branch(xpool_norm,
name='o_fp',
outsize=3,
activation='softmax')
# xout_cat = merge([xout_text,xout_spher,xout_calc],name='o_cat',mode='concat', concat_axis=1)
xout_margin = dense_branch(xpool_norm,
name='o_marg',
outsize=1,
activation='sigmoid')
xout_lob = dense_branch(xpool_norm,
name='o_lob',
outsize=1,
activation='sigmoid')
xout_spic = dense_branch(xpool_norm,
name='o_spic',
outsize=1,
activation='sigmoid')
xout_malig = dense_branch(xpool_norm,
name='o_mal',
outsize=1,
activation='sigmoid')
# xout_numeric = merge([xout_margin, xout_lob, xout_spic, xout_malig],name='o_num',mode='concat',concat_axis=1)
model = Model(input=xin,
output=[
xout_diam, xout_lob, xout_spic, xout_malig,
xout_cad_falsepositive
])
if input_shape[1] == 32:
lr_start = .005
elif input_shape[1] == 64:
lr_start = .001
elif input_shape[1] == 128:
lr_start = 1e-4
elif input_shape[1] == 96:
lr_start = 5e-4
opt = Nadam(lr_start, clipvalue=1.0)
print 'compiling model'
model.compile(optimizer=opt,
loss={
'o_d': 'mse',
'o_lob': 'binary_crossentropy',
'o_spic': 'binary_crossentropy',
'o_mal': 'binary_crossentropy',
'o_fp': 'categorical_crossentropy'
},
loss_weights={
'o_d': 1.0,
'o_lob': 5.0,
'o_spic': 5.0,
'o_mal': 5.0,
'o_fp': 5.0
})
return model
def Dense_net(growth_rate=16,
reduction=0.5,
dropout_rate=0.3,
weight_decay=5e-4,
upsampling_type='upsampling',
init_conv_filters=32):
''' Build the DenseNet model
Args:
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
nb_upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and 'subpixel'. Defines
type of upsampling algorithm used.
input_shape: Only used for shape inference in fully convolutional networks.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
Returns: keras tensor with nb_layers of conv_block appended
'''
if K.image_data_format() == 'channels_last':
img_input = Input(shape=(img_rows, img_cols, chan, 1))
concat_axis = -1
else:
img_input = Input(shape=(1, chan, img_rows, img_cols))
concat_axis = 1
if reduction != 0.0:
assert reduction <= 1.0 and reduction > 0.0, 'reduction value must lie between 0.0 and 1.0'
# compute compression factor
compression = 1.0 - reduction
nb_layers = [4, 8, 16, 8, 4, 2]
growth_rate = 32
# Initial convolution
x1 = Conv3D(64, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
padding='same',
use_bias=False,
kernel_regularizer=l2(weight_decay))(img_input)
x = BatchNormalization(axis=concat_axis)(x1)
x = Activation('relu')(x)
x = AveragePooling3D((2, 2, 2))(x)
print('x:', K.int_shape(x))
# Add dense blocks and transition down block
#stage1
# nb_layers[0]=int((nb_filter[0]-32)/growth_rate)
print('nb_layers:', (nb_layers[0]))
s1_x0, s1_x = down_stage(x, nb_layers[0], 0, growth_rate, dropout_rate,
weight_decay, compression)
print('s10,s11:', K.int_shape(s1_x0), K.int_shape(s1_x))
#stage2
# nb_layers[1]=int((nb_filter[1]-nb_layers[0])/growth_rate)
print('nb_layers:', (nb_layers[1]))
s2_x0, s2_x = down_stage(s1_x, nb_layers[1], 0, growth_rate, dropout_rate,
weight_decay, compression)
print('s20,s21:', K.int_shape(s2_x0), K.int_shape(s2_x))
#stage3
# nb_layers[2]=int((nb_filter[2]-nb_layers[1])/growth_rate)
print('nb_layers:', (nb_layers[2]))
s3_x0 = down_stage(s2_x,
nb_layers[2],
0,
growth_rate,
dropout_rate,
weight_decay,
compression,
pooling=False)
print('s3:', K.int_shape(s3_x0))
#stage4
D1 = Decon_stage(s3_x0,
256,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
weight_decay=weight_decay)
print('D1:', K.int_shape(D1))
con1 = Apply_Attention(D1, s2_x0) #add([D1,s2_x0])
# nb_layers[3]=int((nb_filter[3]-nb_layers[2])/growth_rate)
print('nb_layers:', (nb_layers[3]))
s4_x = up_stage(con1, nb_layers[3], 0, growth_rate, dropout_rate,
weight_decay, compression)
print('s4:', K.int_shape(s4_x))
#stage5
D2 = Decon_stage(s4_x,
128,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
weight_decay=weight_decay)
print('D2:', K.int_shape(D2))
con2 = Apply_Attention(D2, s1_x0) #add([D2,s1_x0])
# nb_layers[4]=int((nb_filter[4]-nb_layers[3])/growth_rate)
print('nb_layers:', (nb_layers[4]))
s5_x = up_stage(con2, nb_layers[4], 0, growth_rate, dropout_rate,
weight_decay, compression)
print('s5:', K.int_shape(s5_x))
#stage6
D3 = Decon_stage(s5_x,
64,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
weight_decay=weight_decay)
print('D3:', K.int_shape(D3))
con3 = Apply_Attention(D3, x1) #add([D3,x1])
print('con3:', K.int_shape(con3))
s6_x = up_stage(con3, nb_layers[5], 0, growth_rate, dropout_rate,
weight_decay, compression)
print('s6:', K.int_shape(s6_x))
#########################################################################
# output1=Deconv3D(1,(8,8,8),strides=(4,4,4),padding='same')(D1)
# output1=Activation('relu')(output1)
output1 = side_out(D1, 4, weight_decay) #,output_shape=(None,1,16,64,64)
output2 = side_out(D2, 2, weight_decay)
output3 = output(D3, weight_decay) #,output_shape=(None,1,16,64,64)
main_out = output(s6_x, weight_decay)
print(K.int_shape(output1))
print(K.int_shape(output2))
print(K.int_shape(output3))
print(K.int_shape(main_out))
model = Model(img_input, [output1, output2, output3, main_out])
# plot_model(model, to_file='Vnet.png',show_shapes=True)
sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='binary_crossentropy',
loss_weights=[0.1, 0.15, 0.25, 0.5],
metrics=[dice_coef
]) #'binary_crossentropy'loss_weights=[0.3,0.6,1.0]
return model
def sofiamodel(Inputs, nclasses, nregressions, dropoutRate=0.05, momentum=0.6):
#image = Input(shape=(25, 25, 25, 1))
x = Inputs[1]
globals = Inputs[0]
totalrecenergy = Inputs[2]
totalrecenergy = Dense(1, kernel_initializer='zeros',
trainable=False)(totalrecenergy)
x = Convolution3D(32,
kernel_size=(1, 1, 1),
strides=(1, 1, 1),
activation='relu',
kernel_initializer='lecun_uniform',
kernel_regularizer=l2(l2_lambda))(x)
x = Dropout(dropoutRate)(x)
x = Convolution3D(12,
kernel_size=(1, 1, 1),
strides=(1, 1, 1),
activation='relu',
kernel_initializer='lecun_uniform')(x)
x = BatchNormalization(momentum=momentum)(x)
preprocessed = Dropout(dropoutRate)(x)
x = Convolution3D(32, (5, 5, 5),
border_mode='same',
kernel_regularizer=l2(l2_lambda))(preprocessed)
x = LeakyReLU()(x)
x = Dropout(dropoutRate)(x)
x = ZeroPadding3D((2, 2, 2))(x)
x = Convolution3D(8, (5, 5, 5),
border_mode='valid',
kernel_regularizer=l2(l2_lambda))(x)
x = LeakyReLU()(x)
x = BatchNormalization()(x)
x = Dropout(dropoutRate)(x)
x = ZeroPadding3D((2, 2, 2))(x)
x = Convolution3D(8, (5, 5, 5),
border_mode='valid',
kernel_regularizer=l2(l2_lambda))(x)
x = LeakyReLU()(x)
x = BatchNormalization()(x)
x = Dropout(dropoutRate)(x)
x = ZeroPadding3D((1, 1, 1))(x)
x = Convolution3D(
8,
(5, 5, 5),
border_mode='valid',
kernel_regularizer=l2(l2_lambda),
)(x)
x = LeakyReLU()(x)
x = BatchNormalization()(x)
x = Dropout(dropoutRate)(x)
x = AveragePooling3D((2, 2, 6))(x)
x = Flatten()(x)
smallshower = Cropping3D(cropping=((4, 4), (4, 4), (0, 0)),
name='muoncrop')(preprocessed)
smallshower = Convolution3D(
32,
(3, 3, 5),
strides=(2, 2, 3),
border_mode='same',
name='muon0',
kernel_regularizer=l2(l2_lambda),
)(smallshower)
smallshower = LeakyReLU()(smallshower)
smallshower = BatchNormalization()(smallshower)
smallshower = Dropout(dropoutRate)(smallshower)
smallshower = Convolution3D(
16,
(1, 1, 5),
strides=(1, 1, 3),
border_mode='same',
name='muon1',
kernel_regularizer=l2(l2_lambda),
)(smallshower)
smallshower = LeakyReLU()(smallshower)
smallshower = BatchNormalization()(smallshower)
smallshower = Dropout(dropoutRate)(smallshower)
smallshower = Convolution3D(
4,
(1, 1, 5),
strides=(1, 1, 3),
border_mode='same',
name='muon2',
kernel_regularizer=l2(l2_lambda),
)(smallshower)
smallshower = LeakyReLU()(smallshower)
smallshower = BatchNormalization()(smallshower)
smallshower = Dropout(dropoutRate)(smallshower)
flattenedsmall = Flatten()(smallshower)
merged = Concatenate()([
globals, x, totalrecenergy, flattenedsmall
]) #add the inputs again in case some don't like the multiplications
x = Dense(32,
activation='relu',
kernel_initializer='lecun_uniform',
name='firstDense')(merged)
x = Dense(1,
activation='linear',
kernel_initializer='lecun_uniform',
use_bias=False)(x)
predictE = Dense(1,
activation='linear',
kernel_initializer='lecun_uniform',
name='pred_E_corr')(x)
predictID = Dense(nclasses,
activation='softmax',
kernel_initializer='lecun_uniform',
name='ID_pred')(x)
predictions = [predictID, predictE]
model = Model(inputs=Inputs, outputs=predictions)
return model
# Initialize model and set model-specific variables
output_units = train_gen.get_output_shape()[-1]
sp_block_filters = [128, 256, 512, 768]
downsize_values = [(6,6), (5,5), (4,4)]
pcn_cell = pcn.PCN_Cell(output_units,
stem_plus_block_filters=sp_block_filters,
time_steps_p_block=[1, 2, 3],
downsize_block_indices=[0, 1, 2],
downsize_values=downsize_values,
name='pcn_cell')
# Use AveragePooling3D with pool_size=(1, X, Y) to reduce size of input frames
input = Input(train_gen.get_input_shape()[1:])
input_aver = AveragePooling3D(pool_size=(3, 2, 2))(input)
final_layer = pcn_cell(input_aver)
if pcn_cell.total_states > 0:
final_layer = final_layer[0]
final_preds = Activation('softmax')(final_layer)
model = Model(input, final_preds)
# Print model summary
model_pcn_layer = model.get_layer('pcn_cell')
gen_utils.print_internal_RNN_layers(model_pcn_layer)
model.summary()
def DenseNet(nb_dense_block=3,
growth_rate=24,
nb_filter=64,
reduction=0.0,
dropout_rate=0.0,
weight_decay=1e-4,
classes=1000,
weights_path=None):
'''Instantiate the DenseNet 121 architecture,
# Arguments
nb_dense_block: number of dense blocks to add to end
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters
reduction: reduction factor of transition blocks.
dropout_rate: dropout rate
weight_decay: weight decay factor
classes: optional number of classes to classify images
weights_path: path to pre-trained weights
# Returns
A Keras model instance.
'''
## nb_dense_block = 3 from paper
## k=24 from paper
## I'm not sure what nb_filter should be
eps = 1.1e-5
# compute compression factor
compression = 1.0 - reduction
## Change the size of images here. Note, I think the 3 represents RGB layers? So a 3d spatio-temopral densenet should be (width, height, num_frames)
## Paper has images scaled to 100Hx100W, 16 frames
# Handle Dimension Ordering for different backends
global concat_axis
concat_axis = 3
img_input = Input(shape=(100, 100, 3, 16), name='data')
# From architecture for ImageNet (Table 1 in the paper)
nb_filter = 64
## The paper says x4 for each layer, 3 total
nb_layers = [4, 4, 4]
## Note: subsample = strides
## Convolution-3D: 7x7x7 conv, stride=2
x = ZeroPadding3D((3, 3, 3), name='conv1_zeropadding')(img_input)
x = Convolution3D(nb_filter,
7,
7,
7,
subsample=(2, 2, 2),
name='conv1',
bias=False)(x)
## Pooling-3D: 3x3x3 avg pool, stride=2
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)
x = Scale(axis=concat_axis, name='conv1_scale')(x)
x = Activation('relu', name='relu1')(x)
x = ZeroPadding3D((1, 1, 1), name='pool1_zeropadding')(x)
x = MaxPooling3D((3, 3, 3), strides=(2, 2, 2), name='pool1')(x)
## Add dense blocks 1, 2
for block_idx in range(nb_dense_block - 1):
stage = block_idx + 2
x, nb_filter = dense_block(x,
stage,
nb_layers[block_idx],
nb_filter,
growth_rate,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
## Add transition_block
x = transition_block(x,
stage,
nb_filter,
compression=compression,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
nb_filter = int(nb_filter * compression)
## Add last dense block: 3 (since we don't have another transition after)
final_stage = stage + 1
x, nb_filter = dense_block(x,
final_stage,
nb_layers[-1],
nb_filter,
growth_rate,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
## Classification Layer
x = BatchNormalization(epsilon=eps,
axis=concat_axis,
name='conv' + str(final_stage) + '_blk_bn')(x)
x = Scale(axis=concat_axis,
name='conv' + str(final_stage) + '_blk_scale')(x)
x = Activation('relu', name='relu' + str(final_stage) + '_blk')(x)
## For our paper, we want a 7x7x4 avg pool instead of a global average pool, assumed to be strides = 1 b/c not specified
## x = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
x = AveragePooling3D((7, 7, 4), strides=(1, 1, 1), name=pool_name_base)(x)
## Fully connected (dense) 2D softmax
## Note: the original 2d densenet paper does a 1000D softmax, but I think this should be 2d since the pooling is no longer global.
x = Dense(classes, name='fc6')(x)
x = Activation('softmax', name='prob')(x)
model = Model(img_input, x, name='densenet')
if weights_path is not None:
model.load_weights(weights_path)
return model