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unet.py
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from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose, Activation
from keras.layers import BatchNormalization
from keras.optimizers import Adam
from keras import backend as K
def dice_coef(y_true, y_pred):
return (2. * K.sum(y_true * y_pred) + 1.) / (K.sum(y_true) + K.sum(y_pred) + 1.)
def unet(num_classes, input_shape, lr_init, lr_decay, vgg_weight_path=None):
img_input = Input(input_shape)
# Block 1
x = Conv2D(64, (3, 3), padding='same', name='block1_conv1')(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(64, (3, 3), padding='same', name='block1_conv2')(x)
x = BatchNormalization()(x)
block_1_out = Activation('relu')(x)
x = MaxPooling2D()(block_1_out)
# Block 2
x = Conv2D(128, (3, 3), padding='same', name='block2_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(128, (3, 3), padding='same', name='block2_conv2')(x)
x = BatchNormalization()(x)
block_2_out = Activation('relu')(x)
x = MaxPooling2D()(block_2_out)
# Block 3
x = Conv2D(256, (3, 3), padding='same', name='block3_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same', name='block3_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same', name='block3_conv3')(x)
x = BatchNormalization()(x)
block_3_out = Activation('relu')(x)
x = MaxPooling2D()(block_3_out)
# Block 4
x = Conv2D(512, (3, 3), padding='same', name='block4_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='block4_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='block4_conv3')(x)
x = BatchNormalization()(x)
block_4_out = Activation('relu')(x)
x = MaxPooling2D()(block_4_out)
# Block 5
x = Conv2D(512, (3, 3), padding='same', name='block5_conv1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='block5_conv2')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='block5_conv3')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
for_pretrained_weight = MaxPooling2D()(x)
# Load pretrained weights.
if vgg_weight_path is not None:
vgg16 = Model(img_input, for_pretrained_weight)
vgg16.load_weights(vgg_weight_path, by_name=True)
# UP 1
x = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = concatenate([x, block_4_out])
x = Conv2D(512, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# UP 2
x = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = concatenate([x, block_3_out])
x = Conv2D(256, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# UP 3
x = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = concatenate([x, block_2_out])
x = Conv2D(128, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(128, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# UP 4
x = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = concatenate([x, block_1_out])
x = Conv2D(64, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# last conv
x = Conv2D(num_classes, (3, 3), activation='softmax', padding='same')(x)
model = Model(img_input, x)
model.compile(optimizer=Adam(lr=lr_init, decay=lr_decay),
loss='categorical_crossentropy',
metrics=[dice_coef])
return model