model.py

import numpy as np 
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
from mode.config import *
from tensorflow.contrib.opt import AdamWOptimizer
from tensorflow.python.keras.optimizers import TFOptimizer
from tensorflow.keras import backend as K

arg = command_arguments()
learning_rate = arg.learning_rate
learning_decay_rate = arg.learning_decay_rate

img_size = (256,256,1) # 256 * 256 grayscale img with 1 channel
dr_rate = 0.6 # never mind
leakyrelu_alpha = 0.3

def unet(pretrained_weights = None,input_size = img_size):
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = LeakyReLU(alpha=leakyrelu_alpha)(conv1)
    #conv1 = Dropout(dr_rate)(conv1) ###
    conv1 = Conv2D(64, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)    
    conv1 = LeakyReLU(alpha=leakyrelu_alpha)(conv1)
    #conv1 = Dropout(dr_rate)(conv1) ###
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    #pool1 = Dropout(dr_rate)(pool1) ### 
    
    conv2 = Conv2D(128, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = BatchNormalization()(conv2)
    conv2 = LeakyReLU(alpha=leakyrelu_alpha)(conv2)
    #conv2 = Dropout(dr_rate)(conv2)###
    conv2 = Conv2D(128, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    conv2 = LeakyReLU(alpha=leakyrelu_alpha)(conv2)    
    #conv2 = Dropout(dr_rate)(conv2)###    
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(256, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = BatchNormalization()(conv3)
    conv3 = LeakyReLU(alpha=leakyrelu_alpha)(conv3)
    #conv3 = Dropout(dr_rate)(conv3) ###
    conv3 = Conv2D(256, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    conv3 = LeakyReLU(alpha=leakyrelu_alpha)(conv3)
    #conv3 = Dropout(dr_rate)(conv3) ###
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(512, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = LeakyReLU(alpha=leakyrelu_alpha)(conv4)    
    #conv4 = Dropout(dr_rate)(conv4) ###
    conv4 = Conv2D(512, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    conv4 = LeakyReLU(alpha=leakyrelu_alpha)(conv4)
    drop4 = Dropout(dr_rate)(conv4) ###
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)    

    conv5 = Conv2D(1024, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = BatchNormalization()(conv5)    
    conv5 = LeakyReLU(alpha=leakyrelu_alpha)(conv5)
    #conv5 = Dropout(dr_rate)(conv5) ###
    conv5 = Conv2D(1024, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv5)
    conv5 = BatchNormalization()(conv5)
    drop5 = LeakyReLU(alpha=leakyrelu_alpha)(conv5)
    #drop5 = Dropout(dr_rate)(conv5) ###

    up6 = Conv2D(512, 2, activation = None, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    up6 = LeakyReLU(alpha=leakyrelu_alpha)(up6)
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(merge6)
    conv6 = BatchNormalization()(conv6)
    conv6 = LeakyReLU(alpha=leakyrelu_alpha)(conv6)
    #conv6 = Dropout(dr_rate)(conv6) ###
    conv6 = Conv2D(512, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv6)
    conv6 = BatchNormalization()(conv6)
    conv6 = LeakyReLU(alpha=leakyrelu_alpha)(conv6)    
    #conv6 = Dropout(dr_rate)(conv6) ###   

    up7 = Conv2D(256, 2, activation = None, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    up7 = BatchNormalization()(up7)    
    up7 = LeakyReLU(alpha=leakyrelu_alpha)(up7)
    up7 = Dropout(dr_rate)(up7) ###
    merge7 = concatenate([conv3,up7], axis = 3)
    conv7 = Conv2D(256, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(merge7)
    conv7 = BatchNormalization()(conv7)    
    conv7 = LeakyReLU(alpha=leakyrelu_alpha)(conv7)    
    #conv7 = Dropout(dr_rate)(conv7) ###
    conv7 = Conv2D(256, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv7)
    conv7 = BatchNormalization()(conv7)
    conv7 = LeakyReLU(alpha=leakyrelu_alpha)(conv7)
    #conv7 = Dropout(dr_rate)(conv7) ###   

    up8 = Conv2D(128, 2, activation = None, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    up8 = BatchNormalization()(up8)
    up8 = LeakyReLU(alpha=0.3)(up8)
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(merge8)
    conv8 = BatchNormalization()(conv8)
    conv8 = LeakyReLU(alpha=0.3)(conv8)
    #conv8 = Dropout(dr_rate)(conv8) ###
    conv8 = Conv2D(128, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv8)
    conv8 = BatchNormalization()(conv8)    
    conv8 = LeakyReLU(alpha=0.3)(conv8)    
    #conv8 = Dropout(dr_rate)(conv8) ###    

    up9 = Conv2D(64, 2, activation = None, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    up9 = BatchNormalization()(up9)
    up9 = LeakyReLU(alpha=leakyrelu_alpha)(up9)
    up9 = Dropout(dr_rate)(up9) ###
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = BatchNormalization()(conv9)
    conv9 = LeakyReLU(alpha=leakyrelu_alpha)(conv9)    
    #conv9 = Dropout(dr_rate)(conv9) ###
    conv9 = Conv2D(64, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)    
    conv9 = LeakyReLU(alpha=leakyrelu_alpha)(conv9)    
    #conv9 = Dropout(dr_rate)(conv9) ###
    conv9 = Conv2D(2, 3, activation = None, padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)
    conv9 = LeakyReLU(alpha=leakyrelu_alpha)(conv9)    
    #conv9 = Dropout(dr_rate)(conv9) ###

    conv10 = Conv2D(3, 1, activation = 'softmax')(conv9)
    model = Model(inputs = inputs, outputs = conv10)   
    model.compile(optimizer = Adam(lr = learning_rate, decay = learning_decay_rate), loss = 'categorical_crossentropy', metrics = ['accuracy'])
    
    #model.summary()

    if(pretrained_weights):
    	model.load_weights(pretrained_weights)

    return model