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DilaLab.py
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DilaLab.py
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from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout,concatenate,BatchNormalization,AveragePooling2D,LeakyReLU,MaxPool2D,add
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau
from data import *
import keras.backend.tensorflow_backend as KTF
from numba import jit
import warnings
warnings.filterwarnings('ignore')
import os
def dice_coef(y_true, y_pred, smooth=1, weight=1):
"""
加权后的dice coefficient
"""
y_true = y_true[:, :, :, -1]
y_pred = y_pred[:, :, :, -1]
intersection = K.sum(y_true * y_pred)
union = K.sum(y_true) + weight * K.sum(y_pred)
return (2. * intersection + smooth) / (union + smooth)
def dice_coef_loss(y_true, y_pred):
"""
目标函数
"""
return 1 - dice_coef(y_true, y_pred)
def f1_score(y_true, y_pred, smooth=1):
"""
f1 score,用于训练过程中选择模型
"""
y_true = y_true[:,:,:,-1]
y_pred = y_pred[:,:,:,-1]
c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
c2 = K.sum(K.round(K.clip(y_pred, 0, 1)))
c3 = K.sum(K.round(K.clip(y_true, 0, 1)))
f1_score = (2*c1+smooth)/(c2+c3+smooth)
return f1_score
class myDilaLab(object):
def __init__(self, img_rows = 512, img_cols = 512):
self.img_rows = img_rows
self.img_cols = img_cols
def load_data(self):
mydata = dataProcess(self.img_rows, self.img_cols)
imgs_train, imgs_mask_train = mydata.load_train_data()
imgs_test = mydata.load_test_data()
return imgs_train, imgs_mask_train, imgs_test
def get_DilaLab(self):
inputs = Input((self.img_rows, self.img_cols, 1))
conv1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = BatchNormalization()(conv2)
conv2 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = BatchNormalization()(conv3)
conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
conv3 = BatchNormalization()(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = BatchNormalization()(conv5)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
conv5 = BatchNormalization()(conv5)
pool5 = MaxPooling2D(pool_size=(2, 2))(conv5)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool5)
conv6 = BatchNormalization()(conv6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = BatchNormalization()(conv6)
pool6 = MaxPooling2D(pool_size=(2, 2))(conv6)
conv71 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(1, 1), kernel_initializer='he_normal')(pool6)
conv72 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(2, 2), kernel_initializer='he_normal')(pool6)
conv73 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(4, 4), kernel_initializer='he_normal')(pool6)
conv74 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(8, 8), kernel_initializer='he_normal')(pool6)
conv75 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(16, 16), kernel_initializer='he_normal')(pool6)
conv76 = Conv2D(1024, 3, activation='relu', padding='same', dilation_rate=(32, 32), kernel_initializer='he_normal')(pool6)
conv7 = add([conv71,conv72,conv73,conv74,conv75,conv76])
up8 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv6, up8], axis=3)
conv8 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = BatchNormalization()(conv8)
conv8 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = BatchNormalization()(conv8)
up9 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv5, up9], axis=3)
conv9 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = BatchNormalization()(conv9)
conv9 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = BatchNormalization()(conv9)
up10 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv9))
merge10 = concatenate([conv4, up10], axis=3)
conv10 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge10)
conv10 = BatchNormalization()(conv10)
conv10 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv10)
conv10 = BatchNormalization()(conv10)
up11 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv10))
merge11 = concatenate([conv3, up11], axis=3)
conv11 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge11)
conv11 = BatchNormalization()(conv11)
conv11 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv11)
conv11 = BatchNormalization()(conv11)
up12 = Conv2D(32, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv11))
merge12 = concatenate([conv2, up12], axis=3)
conv12 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge12)
conv12 = BatchNormalization()(conv12)
conv12 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv12)
conv12 = BatchNormalization()(conv12)
up13 = Conv2D(16, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv12))
merge13 = concatenate([conv1, up13], axis=3)
conv13 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge13)
conv13 = BatchNormalization()(conv13)
conv13 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv13)
conv13 = BatchNormalization()(conv13)
conv13 = Conv2D(1, 1, activation='sigmoid')(conv13)
model = Model(input=inputs, output=conv13)
print(model.summary())
model.load_weights("LYM_vfa/unet3.hdf5")
# model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
model.compile(optimizer = Adam (lr=1e-4), loss = dice_coef_loss, metrics = [f1_score])
return model
def train(self):
print("loading data")
imgs_train, imgs_mask_train, imgs_test = self.load_data()
print("loading data done")
model = self.get_DilaLab()
print("got DilaLab")
# model_checkpoint = ModelCheckpoint('LYM_vfa/DilaLab-{}'.format(datetime.now().strftime('%Y-%m-%d-%H-%M'))+'-{epoch:02d}-{val_f1_score:.2f}.hdf5', monitor='loss',verbose=1, save_best_only=True)
model_checkpoint = ModelCheckpoint('LYM_vfa/unet31.hdf5', monitor='loss',verbose=1, save_best_only=True)
print('Fitting model...')
print(imgs_train.shape,imgs_mask_train.shape)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=1, min_lr=1e-5)
hist = model.fit(imgs_train, imgs_mask_train, batch_size=1, nb_epoch=30, verbose=1,validation_split=0.2, shuffle=True, callbacks=[model_checkpoint,reduce_lr])
with open('LYM_vfa/log_2.txt', 'w') as f:
f.write(str(hist.history)+'\n')
print('predict test data')
print(imgs_test.shape)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
np.save('LYM_vfa\\results\\imgs_mask_test_pred.npy', imgs_mask_test)
def save_img(self):
print("array to image")
imgs = np.load('LYM_vfa\\results\\imgs_mask_test_pred.npy')
for i in range(imgs.shape[0]):
img = imgs[i]
img = array_to_img(img)
img.save("LYM_vfa\\results\\%d.jpg"%(i))
from datetime import datetime
if __name__ == '__main__':
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
start = datetime.now()
myDilaLab =myDilaLab()
myDilaLab.train()
stop = datetime.now()
# print(stop - start)
print('Training time cost: %0.2f(min).' % ((stop - start) / 60))