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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' | |