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models.py
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models.py
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import skimage.io as io
import skimage.color as color
import random as r
import math
import glob
from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.layers import Input, merge, UpSampling2D,BatchNormalization
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
import constants
K.set_image_dim_ordering("th")
# img_size = 240
def DiceCoef(y_true, y_pred, smooth = 0.005):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(K.cast(y_true_f, 'float32') * K.cast(y_pred_f, 'float32'))
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def DiceLoss(y_true, y_pred):
return 1 - DiceCoef(y_true, y_pred)
def UNET(img_size=constants.img_size):
inputs = Input((2, img_size, img_size))
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)
batch1 = BatchNormalization(axis=1)(conv1)
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)
batch1 = BatchNormalization(axis=1)(conv1)
pool1 = MaxPooling2D((2, 2)) (batch1)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)
batch2 = BatchNormalization(axis=1)(conv2)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)
batch2 = BatchNormalization(axis=1)(conv2)
pool2 = MaxPooling2D((2, 2)) (batch2)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)
batch3 = BatchNormalization(axis=1)(conv3)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)
batch3 = BatchNormalization(axis=1)(conv3)
pool3 = MaxPooling2D((2, 2)) (batch3)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)
batch4 = BatchNormalization(axis=1)(conv4)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)
batch4 = BatchNormalization(axis=1)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2)) (batch4)
conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)
batch5 = BatchNormalization(axis=1)(conv5)
conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)
batch5 = BatchNormalization(axis=1)(conv5)
up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)
up6 = concatenate([up6, conv4], axis=1)
conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)
batch6 = BatchNormalization(axis=1)(conv6)
conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)
batch6 = BatchNormalization(axis=1)(conv6)
up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)
up7 = concatenate([up7, conv3], axis=1)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)
batch7 = BatchNormalization(axis=1)(conv7)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)
batch7 = BatchNormalization(axis=1)(conv7)
up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)
up8 = concatenate([up8, conv2], axis=1)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)
batch8 = BatchNormalization(axis=1)(conv8)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)
batch8 = BatchNormalization(axis=1)(conv8)
up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)
up9 = concatenate([up9, conv1], axis=1)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)
batch9 = BatchNormalization(axis=1)(conv9)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)
batch9 = BatchNormalization(axis=1)(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=constants.LR), loss=DiceLoss, metrics=[DiceCoef])
return model
def UNET_NEC3(img_size_nec=64):
inputs = Input((1, img_size_nec, img_size_nec))
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)
batch1 = BatchNormalization(axis=1)(conv1)
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)
batch1 = BatchNormalization(axis=1)(conv1)
pool1 = MaxPooling2D((2, 2)) (batch1)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)
batch2 = BatchNormalization(axis=1)(conv2)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)
batch2 = BatchNormalization(axis=1)(conv2)
pool2 = MaxPooling2D((2, 2)) (batch2)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)
batch3 = BatchNormalization(axis=1)(conv3)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)
batch3 = BatchNormalization(axis=1)(conv3)
pool3 = MaxPooling2D((2, 2)) (batch3)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)
batch5 = BatchNormalization(axis=1)(conv5)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch5)
batch5 = BatchNormalization(axis=1)(conv5)
up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch5)
up7 = concatenate([up7, conv3], axis=1)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)
batch7 = BatchNormalization(axis=1)(conv7)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)
batch7 = BatchNormalization(axis=1)(conv7)
up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)
up8 = concatenate([up8, conv2], axis=1)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)
batch8 = BatchNormalization(axis=1)(conv8)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)
batch8 = BatchNormalization(axis=1)(conv8)
up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)
up9 = concatenate([up9, conv1], axis=1)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)
batch9 = BatchNormalization(axis=1)(conv9)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)
batch9 = BatchNormalization(axis=1)(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=constants.LR), loss=DiceLoss, metrics=[DiceCoef])
return model