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from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, BatchNormalization, MaxPooling2D, Dropout, Concatenate, UpSampling2D
class Unet2D:
def __init__(self, n_filters, input_dim_x, input_dim_y, num_channels):
self.input_dim_x = input_dim_x
self.input_dim_y = input_dim_y
self.n_filters = n_filters
self.num_channels = num_channels
def get_unet_model_5_levels(self):
unet_input = Input(shape=(self.input_dim_x, self.input_dim_y, self.num_channels))
conv1 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(unet_input)
conv1 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(conv1)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(conv2)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(conv3)
conv3 = BatchNormalization()(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(conv4)
conv4 = BatchNormalization()(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(self.n_filters*16, kernel_size=3, activation='relu', padding='same')(pool4)
conv5 = Conv2D(self.n_filters*16, kernel_size=3, activation='relu', padding='same')(conv5)
conv5 = BatchNormalization()(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(self.n_filters*16, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5))
concat6 = Concatenate()([drop4, up6])
conv6 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(concat6)
conv6 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(conv6)
conv6 = BatchNormalization()(conv6)
up7 = Conv2D(self.n_filters*8, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
concat7 = Concatenate()([conv3, up7])
conv7 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(concat7)
conv7 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(conv7)
conv7 = BatchNormalization()(conv7)
up8 = Conv2D(self.n_filters*4, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7))
concat8 = Concatenate()([conv2, up8])
conv8 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(concat8)
conv8 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(conv8)
conv8 = BatchNormalization()(conv8)
up9 = Conv2D(self.n_filters*2, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8))
concat9 = Concatenate()([conv1, up9])
conv9 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(concat9)
conv9 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(conv9)
conv9 = BatchNormalization()(conv9)
conv10 = Conv2D(3, kernel_size=1, activation='sigmoid', padding='same')(conv9)
return Model(outputs=conv10, inputs=unet_input), 'unet_model_5_levels'
def get_unet_model_4_levels(self):
unet_input = Input(shape=(self.input_dim_x, self.input_dim_y, self.num_channels))
conv1 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(unet_input)
conv1 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(conv1)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(conv2)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(conv3)
conv3 = BatchNormalization()(conv3)
drop3 = Dropout(0.5)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)
conv4 = Conv2D(self.n_filters*16, kernel_size=3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(self.n_filters*16, kernel_size=3, activation='relu', padding='same')(conv4)
conv4 = BatchNormalization()(conv4)
drop4 = Dropout(0.5)(conv4)
up5 = Conv2D(self.n_filters*16, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop4))
concat5 = Concatenate()([drop3, up5])
conv5 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(concat5)
conv5 = Conv2D(self.n_filters*8, kernel_size=3, activation='relu', padding='same')(conv5)
conv5 = BatchNormalization()(conv5)
up6 = Conv2D(self.n_filters*8, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv5))
concat6 = Concatenate()([conv2, up6])
conv6 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(concat6)
conv6 = Conv2D(self.n_filters*4, kernel_size=3, activation='relu', padding='same')(conv6)
conv6 = BatchNormalization()(conv6)
up7 = Conv2D(self.n_filters*4, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
concat7 = Concatenate()([conv1, up7])
conv7 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(concat7)
conv7 = Conv2D(self.n_filters*2, kernel_size=3, activation='relu', padding='same')(conv7)
conv7 = BatchNormalization()(conv7)
conv9 = Conv2D(3, kernel_size=1, activation='sigmoid', padding='same')(conv7)
return Model(outputs=conv9, inputs=unet_input), 'unet_model_4_levels'
def get_unet_model_yuanqing(self):
# Model inspired by https://github.com/yuanqing811/ISIC2018
unet_input = Input(shape=(self.input_dim_x, self.input_dim_y, self.num_channels))
conv1 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(unet_input)
conv1 = Conv2D(self.n_filters, kernel_size=3, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(self.n_filters * 2, kernel_size=3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(self.n_filters * 2, kernel_size=3, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(conv3)
conv3 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(conv4)
conv4 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(pool4)
conv5 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(conv5)
conv5 = Conv2D(self.n_filters * 8, kernel_size=3, activation='relu', padding='same')(conv5)
up6 = Conv2D(self.n_filters * 4, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv5))
feature4 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(conv4)
concat6 = Concatenate()([feature4, up6])
conv6 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(concat6)
conv6 = Conv2D(self.n_filters * 4, kernel_size=3, activation='relu', padding='same')(conv6)
up7 = Conv2D(self.n_filters * 2, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
feature3 = Conv2D(self.n_filters * 2, kernel_size=3, activation='relu', padding='same')(conv3)
concat7 = Concatenate()([feature3, up7])
conv7 = Conv2D(self.n_filters * 2, kernel_size=3, activation='relu', padding='same')(concat7)
conv7 = Conv2D(self.n_filters * 2, kernel_size=3, activation='relu', padding='same')(conv7)
up8 = Conv2D(self.n_filters * 1, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7))
feature2 = Conv2D(self.n_filters * 1, kernel_size=3, activation='relu', padding='same')(conv2)
concat8 = Concatenate()([feature2, up8])
conv8 = Conv2D(self.n_filters * 1, kernel_size=3, activation='relu', padding='same')(concat8)
conv8 = Conv2D(self.n_filters * 1, kernel_size=3, activation='relu', padding='same')(conv8)
up9 = Conv2D(int(self.n_filters / 2), 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8))
feature1 = Conv2D(int(self.n_filters / 2), kernel_size=3, activation='relu', padding='same')(conv1)
concat9 = Concatenate()([feature1, up9])
conv9 = Conv2D(int(self.n_filters / 2), kernel_size=3, activation='relu', padding='same')(concat9)
conv9 = Conv2D(int(self.n_filters / 2), kernel_size=3, activation='relu', padding='same')(conv9)
conv9 = Conv2D(3, kernel_size=3, activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, kernel_size=1, activation='sigmoid')(conv9)
return Model(outputs=conv10, inputs=unet_input), 'unet_model_yuanqing'
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