Unet.py

from keras.layers import Input,Conv2D,MaxPooling2D,BatchNormalization,Conv2DTranspose,concatenate,Reshape,Permute,Activation
from keras.models import Model

def U_Net(width, height, depth, classes=2,weightsPath=None):
        
    data_shape = height*width
    
    inputs = Input((depth,width, height))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = BatchNormalization(axis=1)(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    conv1 = BatchNormalization(axis=1)(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = BatchNormalization(axis=1)(conv2)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    conv2 = BatchNormalization(axis=1)(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = BatchNormalization(axis=1)(conv3)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    conv3 = BatchNormalization(axis=1)(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = BatchNormalization(axis=1)(conv4)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    conv4 = BatchNormalization(axis=1)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
    
    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=1)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
    
    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=1)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
    
    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=1)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
    
    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=1)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
    
    conv10 = Conv2D(classes, (3, 3), padding='same')(conv9)
             
    reshape = Reshape((classes, data_shape), input_shape=(classes, height, width))(conv10)
    perm = Permute((2, 1))(reshape)
    softmax = Activation("softmax")(perm)
    
    model = Model(inputs=[inputs], outputs=[softmax])
    
    if weightsPath is not None:
        model.load_weights(weightsPath)
    
    return model 

def U_Net_ChannelLast(width, height, depth, classes=2,weightsPath=None):
        
    data_shape = height*width
    
    inputs = Input((width, height,depth))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = BatchNormalization(axis=3)(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    conv1 = BatchNormalization(axis=3)(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = BatchNormalization(axis=3)(conv2)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    conv2 = BatchNormalization(axis=3)(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = BatchNormalization(axis=3)(conv3)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    conv3 = BatchNormalization(axis=3)(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = BatchNormalization(axis=3)(conv4)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    conv4 = BatchNormalization(axis=3)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
    
    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
    
    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
    
    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
    
    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
    
    conv10 = Conv2D(classes, (3, 3), padding='same')(conv9)
             
    reshape = Reshape((classes, data_shape), input_shape=(height, width,classes))(conv10)
    perm = Permute((2, 1))(reshape)
    softmax = Activation("softmax")(perm)
    
    model = Model(inputs=[inputs], outputs=[softmax])
    
    if weightsPath is not None:
        model.load_weights(weightsPath)
    
    return model