def get_unet():
with tf.device('/device:GPU:0'):
inputs = Input((192, 192, 3))
conv1 = initial_conv_block(inputs, weight_decay=5e-4)
conv1 = bottleneck_block(conv1, filters=32, cardinality=32, strides=1, weight_decay=5e-4)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = bottleneck_block(pool1, filters=32, cardinality=32, strides=1, weight_decay=5e-4)
conv2 = bottleneck_block(conv2, filters=64, cardinality=32, strides=1, weight_decay=5e-4)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = bottleneck_block(pool2, filters=64, cardinality=32, strides=1, weight_decay=5e-4)
conv3 = bottleneck_block(conv3, filters=128, cardinality=32, strides=1, weight_decay=5e-4)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = bottleneck_block(pool3, filters=128, cardinality=32, strides=1, weight_decay=5e-4)
conv4 = bottleneck_block(conv4, filters=256, cardinality=32, strides=1, weight_decay=5e-4)
drop4 = Dropout(0.2)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = bottleneck_block(pool4, filters=256, cardinality=32, strides=1, weight_decay=5e-4)
conv5 = bottleneck_block(conv5, filters=256, cardinality=32, strides=1, weight_decay=5e-4)
drop5 = Dropout(0.2)(conv5)
with tf.device('/device:GPU:1'):
up6 = Conv2D(128, (3,3), activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = keras.layers.Concatenate()([drop4, up6])
conv6 = bottleneck_block(merge6, filters=128, cardinality=32, strides=1, weight_decay=5e-4)
conv6 = bottleneck_block(conv6, filters=128, cardinality=32, strides=1, weight_decay=5e-4)
up7 = Conv2D(64, (3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = keras.layers.Concatenate()([conv3, up7])
conv7 = bottleneck_block(merge7, filters=64, cardinality=32, strides=1, weight_decay=5e-4)
conv7 = bottleneck_block(conv7, filters=64, cardinality=32, strides=1, weight_decay=5e-4)
up8 = Conv2D(32, (3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = keras.layers.Concatenate()([conv2, up8])
conv8 = bottleneck_block(merge8, filters=32, cardinality=32, strides=1, weight_decay=5e-4)
conv8 = bottleneck_block(conv8, filters=32, cardinality=32, strides=1, weight_decay=5e-4)
up9 = Conv2D(16, (3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = keras.layers.Concatenate()([conv1, up9])
conv9 = Residual(48, 16, merge9)
conv9 = Residual(16, 4, conv9)
conv9 = Residual(4, 1, conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs, conv10)
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.03, momentum=0.9, nesterov=True), metrics=[dice_coef,jaccard_coef])
return model
up9 = Conv2D(16,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal',
name='UpConv4')(UpSampling2D(size=(2, 2),
name='Up4')(conv8_1))
merge9 = keras.layers.Concatenate(name='Concat4')([conv1, up9])
conv9 = Residual17(48, 16, merge9)
conv10 = Residual18(16, 2, conv9)
conv10 = Residual19(2, 1, conv10)
conv11 = Conv2D(1, 1, activation='sigmoid', name='Output')(conv10)
with tf.device('/device:GPU:3'):
init = initial_conv_block(input, weight_decay=5e-4)
#x1 = ResidualR(32, 64, init) #192x192x64
#x1 = ResidualR(64, 64, x1)
#x1 = ResidualR(64, 64, x1) #192x192x64
x1 = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(init)
x1 = BatchNormalization()(x1)
x1 = layers.LeakyReLU()(x1)
x1concat = keras.layers.Concatenate()([x1, conv9]) #192x192x80
x1se = squeeze_excite_block(x1concat)
x1conv1 = SeparableConv2D(80, (1, 1),
padding='same',
kernel_initializer='he_normal')(x1se)
x1conv1 = layers.LeakyReLU()(x1conv1)
x1conv2 = Conv2D(64, (1, 1),
def focusnet():
input = Input((192, 256, 3))
conv1 = initial_conv_block(input) #512
pool1 = _residual_block(basic_block, filters=32, repetitions=1, is_first_layer=False)(conv1) #256
conv2 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=True)(pool1) #256
pool2 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=False)(conv2) #128
conv3 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=True)(pool2) #128
pool3 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=False)(conv3) #64
conv4 = _residual_block(basic_block, filters=256, repetitions=1, is_first_layer=True)(pool3) #64
drop4 = Dropout(0.2)(conv4)
up5 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop4)) #128
merge5 = keras.layers.Concatenate()([conv3,up5])
conv5 = _residual_block(basic_block, filters=256, repetitions=1, is_first_layer=True)(merge5) #128
up6 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv5)) #256
merge6 = keras.layers.Concatenate()([conv2,up6])
conv6 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=True)(merge6) #256
up7 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6)) #512
merge7 = keras.layers.Concatenate()([conv1,up7])
conv7 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=True)(merge7) #512
conv1r = _conv_bn_relu(filters=64, kernel_size=(7, 7), strides=(1, 1))(input) #512
block1 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=True)(conv1r) #512
se1 = squeeze_excite_block(block1)
gate1 = Activation('sigmoid')(conv7)
block1concat = keras.layers.Multiply()([se1, gate1]) #512
block1se = squeeze_excite_block(block1concat)
block1b = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=False)(block1se) #256
block2 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=True)(block1b) #256
se2 = squeeze_excite_block(block2)
gate2 = Activation('sigmoid')(conv6)
block2concat = keras.layers.Multiply()([se2, gate2]) #256
block2se = squeeze_excite_block(block2concat)
block2b = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=False)(block2se) #128
block3 = _residual_block(basic_block, filters=256, repetitions=1, is_first_layer=True)(block2b) #128
se3 = squeeze_excite_block(block3)
gate3 = Activation('sigmoid')(conv5)
block3concat = keras.layers.Multiply()([se3, gate3]) #128
block3se = squeeze_excite_block(block3concat)
block3b = _residual_block(basic_block, filters=256, repetitions=1, is_first_layer=False)(block3se) # 64
block4 = _residual_block(basic_block, filters=512, repetitions=1, is_first_layer=True)(block3b) #64
block4se = squeeze_excite_block(block4)
block4b = _residual_block(basic_block, filters=512, repetitions=1, is_first_layer=False)(block4se) #32
up2_5 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(block4b)) #64
merge2_5 = keras.layers.Concatenate()([block3b,up2_5])
conv2_5 = _residual_block(basic_block, filters=256, repetitions=1, is_first_layer=True)(merge2_5) #64
out1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (8,8))(conv2_5))
out1 = Conv2D(1, 1, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(out1)
up2_6 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv2_5)) #128
merge2_6 = keras.layers.Concatenate()([block2b,up2_6])
conv2_6 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=True)(merge2_6) #128
out2 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (4,4))(conv2_6))
out2 = Conv2D(1, 1, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(out2)
up2_7 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv2_6)) #256
merge2_7 = keras.layers.Concatenate()([block1b,up2_7])
conv2_7 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=True)(merge2_7) #256
out3 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv2_7))
out3 = Conv2D(1, 1, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(out3)
up2_8 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv2_7)) #512
merge2_8 = keras.layers.Concatenate()([conv1r,up2_8])
conv2_8 = _residual_block(basic_block, filters=32, repetitions=1, is_first_layer=True)(merge2_8) #512
conv2_8 = _residual_block(basic_block, filters=16, repetitions=1, is_first_layer=True)(conv2_8)
conv2_8 = _residual_block(basic_block, filters=4, repetitions=1, is_first_layer=True)(conv2_8)
out4 = Conv2D(1, 1, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(conv2_8)
out_concat = keras.layers.Concatenate()([out1, out2, out3, out4])
out_concat = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(out_concat)
out = Conv2D(1, 1, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(out_concat)
model = Model(inputs=input, outputs=out)
model.compile(optimizer = SGD(lr=0.0005, momentum=0.9, nesterov=True), loss = losses.focal_tversky, metrics = [losses.tp, losses.tn, losses.dsc, losses.jacard_coef, 'accuracy'])
model.summary()
return model
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(upres2))
merge8 = keras.layers.Add()([res1, up8])
upres1 = seperableConv_bottleneck_block_with_se(merge8,
filters=32,
cardinality=32,
strides=1,
weight_decay=5e-4)
upres1 = seperableConv_bottleneck_block_with_se(upres1,
filters=1,
cardinality=1,
strides=1,
weight_decay=5e-4)
outputConvAutoEncoder = Conv2D(1, (1, 1), activation='sigmoid')(upres1)
#-------------------------------------------------------------------------------------------------------------------------------------
x = initial_conv_block(input_image, weight_decay=5e-4)
x = bottleneck_block_with_se(x,
filters=96,
cardinality=32,
strides=1,
weight_decay=5e-4) # 196x196
x = bottleneck_block_with_se(x,
filters=96,
cardinality=32,
strides=1,
weight_decay=5e-4)
x = bottleneck_block_with_se(x,
filters=96,
cardinality=32,
strides=1,
def get_unet():
input = Input((256, 256, 3))
conv1 = initial_conv_block(input) #512
pool1 = _residual_block(basic_block,
filters=32,
repetitions=1,
is_first_layer=False)(conv1) #256
conv2 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=True)(pool1) #256
pool2 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=False)(conv2) #128
conv3 = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=True)(pool2) #128
pool3 = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=False)(conv3) #64
conv4 = _residual_block(basic_block,
filters=256,
repetitions=1,
is_first_layer=True)(pool3) #64
drop4 = Dropout(0.2)(conv4)
up5 = Conv2D(256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop4)) #128
merge5 = keras.layers.Concatenate()([conv3, up5])
conv5 = _residual_block(basic_block,
filters=256,
repetitions=1,
is_first_layer=True)(merge5) #128
up6 = Conv2D(128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv5)) #256
merge6 = keras.layers.Concatenate()([conv2, up6])
conv6 = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=True)(merge6) #256
up7 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6)) #512
merge7 = keras.layers.Concatenate()([conv1, up7])
conv7 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=True)(merge7) #512
conv1r = _conv_bn_relu(filters=64, kernel_size=(7, 7),
strides=(1, 1))(input) #512
block1 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=True)(conv1r) #512
se1 = squeeze_excite_block(block1)
gate1 = Activation('sigmoid')(conv7)
block1concat = keras.layers.Multiply()([se1, gate1]) #512
block1se = squeeze_excite_block(block1concat)
block1b = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=False)(block1se) #256
block2 = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=True)(block1b) #256
se2 = squeeze_excite_block(block2)
gate2 = Activation('sigmoid')(conv6)
block2concat = keras.layers.Multiply()([se2, gate2]) #256
block2se = squeeze_excite_block(block2concat)
block2b = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=False)(block2se) #128
block3 = _residual_block(basic_block,
filters=256,
repetitions=1,
is_first_layer=True)(block2b) #128
se3 = squeeze_excite_block(block3)
gate3 = Activation('sigmoid')(conv5)
block3concat = keras.layers.Multiply()([se3, gate3]) #128
block3se = squeeze_excite_block(block3concat)
block3b = _residual_block(basic_block,
filters=256,
repetitions=1,
is_first_layer=False)(block3se) # 64
block4 = _residual_block(basic_block,
filters=512,
repetitions=1,
is_first_layer=True)(block3b) #64
block4se = squeeze_excite_block(block4)
block4b = _residual_block(basic_block,
filters=512,
repetitions=1,
is_first_layer=False)(block4se) #32
up2_5 = Conv2D(256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(block4b)) #64
merge2_5 = keras.layers.Concatenate()([block3b, up2_5])
conv2_5 = _residual_block(basic_block,
filters=256,
repetitions=1,
is_first_layer=True)(merge2_5) #64
up2_6 = Conv2D(128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv2_5)) #128
merge2_6 = keras.layers.Concatenate()([block2b, up2_6])
conv2_6 = _residual_block(basic_block,
filters=128,
repetitions=1,
is_first_layer=True)(merge2_6) #128
up2_7 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv2_6)) #256
merge2_7 = keras.layers.Concatenate()([block1b, up2_7])
conv2_7 = _residual_block(basic_block,
filters=64,
repetitions=1,
is_first_layer=True)(merge2_7) #256
up2_8 = Conv2D(32,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv2_7)) #512
merge2_8 = keras.layers.Concatenate()([conv1r, up2_8])
conv2_8 = _residual_block(basic_block,
filters=32,
repetitions=1,
is_first_layer=True)(merge2_8) #512
conv2_8 = _residual_block(basic_block,
filters=16,
repetitions=1,
is_first_layer=True)(conv2_8)
conv2_8 = _residual_block(basic_block,
filters=4,
repetitions=1,
is_first_layer=True)(conv2_8)
out = Conv2D(1,
1,
activation='sigmoid',
padding='same',
kernel_initializer='he_normal')(conv2_8)
model = Model(inputs=input, outputs=out)
model.compile(optimizer=Adam(lr=1e-4),
loss=dice_coef_loss,
metrics=[dice_coef, jaccard_coef, 'acc'])
model.summary()
return model