# -*- coding: utf-8 -*-

"""

Created on Sun Apr 21 13:49:32 2019

@author: Winham

Unet.py: Unet模型定义

"""

from keras.models import Model

from keras.layers import Input, core, Dropout, concatenate

from keras.layers.convolutional import Conv1D, MaxPooling1D, UpSampling1D

def Unet(nClasses, optimizer=None, input_length=1800, nChannels=1):

    inputs = Input((input_length, nChannels))

    conv1 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)

    conv1 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)

    pool1 = MaxPooling1D(pool_size=2)(conv1)

    conv2 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)

    conv2 = Dropout(0.2)(conv2)

    conv2 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)

    pool2 = MaxPooling1D(pool_size=2)(conv2)

    conv3 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)

    conv3 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)

    pool3 = MaxPooling1D(pool_size=2)(conv3)

    conv4 = Conv1D(128, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)

    conv4 = Dropout(0.5)(conv4)

    conv4 = Conv1D(128, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)

    up1 = Conv1D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling1D(size=2)(conv4))

    merge1 = concatenate([up1, conv3], axis=-1)

    conv5 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(merge1)

    conv5 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)

    up2 = Conv1D(32, 2, activation='relu', padding='same', kernel_initializer = 'he_normal')(UpSampling1D(size=2)(conv5))

    merge2 = concatenate([up2, conv2], axis=-1)

    conv6 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer = 'he_normal')(merge2)

    conv6 = Dropout(0.2)(conv6)

    conv6 = Conv1D(32, 32, activation='relu', padding='same')(conv6)

    up3 = Conv1D(16, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling1D(size=2)(conv6))

    merge3 = concatenate([up3, conv1], axis=-1)

    conv7 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(merge3)

    conv7 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    conv8 = Conv1D(nClasses, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    conv8 = core.Reshape((nClasses, input_length))(conv8)

    conv8 = core.Permute((2, 1))(conv8)

    conv9 = core.Activation('softmax')(conv8)

    model = Model(inputs=inputs, outputs=conv9)

    if not optimizer is None:

        model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=['accuracy'])

    return model

if __name__ == '__main__':

    print('\nSummarize the model:\n')

    model = Unet(3)

    model.summary()

    print('\nEnd for summary.\n')