Я пытаюсь запустить сеть для маскировки данных изображения с его метками. Часть управления данными в коде прошла хорошо. Здесь я борюсь с этим кодом, когда я запускаю unet.py. Код выполняет и выдает предупреждение и дальнейшая остановка процесса без получения результатов. хотел знать, если его версия библиотеки проблема? Не нашел ничего похожего на это. В более поздней строке я отлаживал, все еще не выдавая никакой подсказки. Выполнение кода останавливается на « с использованием серверной части TensorFlow ». без ошибок и приступает к обучению модели и сохранению фазы модели. Вся структура каталогов была проверена и в правильном порядке.
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.preprocessing.image import array_to_img
import cv2
from data import *
class myUnet(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_unet(self):
inputs = Input((self.img_rows, self.img_cols, 3))
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
# print(conv1)
conv1 = BatchNormalization()(conv1)
print ("conv1 shape:", conv1.shape)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
conv1 = BatchNormalization()(conv1)
print ("conv1 shape:", conv1.shape)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
print ("pool1 shape:", pool1.shape)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
print ("conv2 shape:", conv2.shape)
conv2 = BatchNormalization()(conv2)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
print ("conv2 shape:", conv2.shape)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
print ("pool2 shape:", pool2.shape)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
print ("conv3 shape:", conv3.shape)
conv3 = BatchNormalization()(conv3)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
print ("conv3 shape:", conv3.shape)
conv3 = BatchNormalization()(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
print ("pool3 shape:", pool3.shape)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = BatchNormalization()(conv4)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
conv4 = BatchNormalization()(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = BatchNormalization()(conv5)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
conv5 = BatchNormalization()(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
up6 = BatchNormalization()(up6)
merge6 = concatenate([drop4, up6], axis=3)
print(up6)
print(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
print(conv6)
conv6 = BatchNormalization()(conv6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
print(conv6)
conv6 = BatchNormalization()(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
up7 = BatchNormalization()(up7)
merge7 = concatenate([conv3, up7], axis=3)
print(up7)
print(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = BatchNormalization()(conv7)
print(conv7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
print(conv7)
conv7 = BatchNormalization()(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
up8 = BatchNormalization()(up8)
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = BatchNormalization()(conv8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = BatchNormalization()(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
up9 = BatchNormalization()(up9)
merge9 = concatenate([conv1, up9], axis=3)
print(up9)
print(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = BatchNormalization()(conv9)
print(conv9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = BatchNormalization()(conv9)
print(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = BatchNormalization()(conv9)
print ("conv9 shape:", conv9.shape)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
print(conv10)
model = Model(inputs=inputs, outputs=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
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_unet()
print("got unet")
model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', verbose=1, save_best_only=True)
print('Fitting model...')
model.fit(imgs_train, imgs_mask_train, batch_size=4, epochs=100, verbose=1,
validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])
print('predict test data')
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
np.save('./data/results/imgs_mask_test.npy', imgs_mask_test)
def save_img(self):
print("array to image")
imgs = np.load('./data/results/imgs_mask_test.npy')
piclist = []
for line in open("./data/results/pic.txt"):
line = line.strip()
picname = line.split('/')[-1]
piclist.append(picname)
print(len(piclist))
for i in range(imgs.shape[0]):
path = "./data/results/" + piclist[i]
img = imgs[i]
img = array_to_img(img)
img.save(path)
cv_pic = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
cv_pic = cv2.resize(cv_pic,(1918,1280),interpolation=cv2.INTER_CUBIC)
binary, cv_save = cv2.threshold(cv_pic, 127, 255, cv2.THRESH_BINARY)
cv2.imwrite(path, cv_save)
def load_model_weights(self, model):
model.load_weights('./data/unet.hdf5')
if __name__ == '__main__':
print("going to create model")
myunet = myUnet()
print("model created.. going to retreive model")
model = myunet.get_unet()
# model.summary()
# plot_model(model, to_file='model.png')
# myunet.load_model_weights(model)
print("train model")
myunet.train()
print("save model")
myunet.save_img()
WARNING:tensorflow:From unet.py:4: The name tf.keras.layers.CuDNNGRU is deprecated. Please use tf.compat.v1.keras.layers.CuDNNGRU instead.
WARNING:tensorflow:From unet.py:4: The name tf.keras.layers.CuDNNLSTM is deprecated. Please use tf.compat.v1.keras.layers.CuDNNLSTM instead.
Using TensorFlow backend.