def build_resnet(input_shape, block_fn, repetitions, plot_model_f):
"""Builds a custom ResNet like architecture.
Args:
input_shape: The input shape in the form (nb_channels, nb_rows, nb_cols)
num_outputs: The number of outputs at final softmax layer
block_fn: The block function to use. This is either `basic_block` or `bottleneck`.
The original paper used basic_block for layers < 50
repetitions: Number of repetitions of various block units.
At each block unit, the number of filters are doubled and the input size is halved
Returns:
The resnet part of the model.
"""
kr._handle_dim_ordering()
if len(input_shape) != 3:
raise Exception(
"Input shape should be a tuple (nb_channels, nb_rows, nb_cols)")
# Permute dimension order if necessary
if K.image_dim_ordering() == 'tf':
input_shape = (input_shape[1], input_shape[2], input_shape[0])
# Load function from str if needed.
block_fn = kr._get_block(block_fn)
input = Input(shape=input_shape)
conv1 = kr._conv_bn_relu(filters=64, kernel_size=(7, 7),
strides=(2, 2))(input)
pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2),
padding="same")(conv1)
block = pool1
filters = 64
for i, r in enumerate(repetitions):
block = kr._residual_block(block_fn,
filters=filters,
repetitions=r,
is_first_layer=(i == 0))(block)
filters *= 2
# Last activation
block = kr._bn_relu(block)
# Classifier block
block_shape = K.int_shape(block)
pool2 = AveragePooling2D(pool_size=(block_shape[kr.ROW_AXIS],
block_shape[kr.COL_AXIS]),
strides=(1, 1))(block)
model = Model(inputs=input, outputs=pool2)
# plot_model(model, to_file='../model_resnet_inner.png', show_shapes=True)
return model
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
conv7_1 = Residual14(64, 32, conv7)
up8 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', name='UpConv3')(UpSampling2D(size = (2,2), name='Up3')(conv7_1))
merge8 = keras.layers.Concatenate(name='Concat3')([conv2,up8])
conv8 = Residual15(96, 32, merge8)
conv8_1 = Residual16(32, 16, conv8)
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)
conv1r = _conv_bn_relu(filters=64, kernel_size=(7, 7), strides=(1, 1))(input)
block1 = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=True)(conv1r)
block1concat = keras.layers.Concatenate()([block1, conv9])
block1se = squeeze_excite_block(block1concat)
block1conv1 = Conv2D(64, (3,3), padding = 'same', kernel_initializer = 'he_normal')(block1se)
block1conv1 = BatchNormalization(axis=CHANNEL_AXIS)(block1conv1)
block1conv1 = layers.LeakyReLU()(block1conv1)
block1conv2 = Conv2D(64, (3,3), padding = 'same', kernel_initializer = 'he_normal')(block1conv1)
block1conv2 = BatchNormalization(axis=CHANNEL_AXIS)(block1conv2)
block1conv2 = layers.LeakyReLU()(block1conv2)
block1b = _residual_block(basic_block, filters=64, repetitions=1, is_first_layer=False)(block1conv2)
block2 = _residual_block(basic_block, filters=128, repetitions=1, is_first_layer=True)(block1b)
block2concat = keras.layers.Concatenate()([block2, conv8])
block2se = squeeze_excite_block(block2concat)
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
