def exec(self):
try:
log.info('[START] {}'.format("exec"))
# fileInfoPattrn = '{}/{}/{}'.format(globalVar['inpPath'], serviceName, '1.csv')
# fileInfo = glob.glob(fileInfoPattrn)
# if (len(fileInfo) < 1): raise Exception("[ERROR] fileInfo : {} : {}".format("자료를 확인해주세요.", fileInfoPattrn))
# saveFile = '{}/{}_{}'.format(globalVar['figPath'], serviceName, '2021_nagano_S1_01_raw.png')
# log.info('[CHECK] saveFile : {}'.format(saveFile))
# breakpoint()
fileInfoPattrn = '{}/{}/{}'.format(
globalVar['inpPath'], serviceName,
'coms_data/coms_mi_le2_sst_data1.txt')
fileInfo = glob.glob(fileInfoPattrn)
if (len(fileInfo) < 1):
raise Exception("[ERROR] fileInfo : {} : {}".format(
"자료를 확인해주세요.", fileInfoPattrn))
data = pd.read_csv(fileInfo[0], header=None)
# 이미지 그리기
fileName = os.path.basename(fileInfo[0])
saveImg = '{}/{}_{}.png'.format(globalVar['figPath'], serviceName,
fileName)
plt.pcolormesh(data)
plt.clim()
plt.colorbar()
plt.savefig(saveImg, dpi=600, bbox_inches='tight')
plt.show()
# patch_size = (33, 33)
patch_size = data.shape
input_shape = (patch_size[0], patch_size[1], 1)
batch_size = 64
input_img = Input(shape=input_shape)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(input_img)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
model.compile(loss=MeanSquaredError(),
optimizer=Adam(),
metrics=['accuracy'])
# metrics=[PSNRLoss])
# breakpoint()
# img = cv2.imread('test.jpg')
# img = cv2.resize(img, (320, 240))
# img = np.reshape(data, [patch_size[0], patch_size[1]])
img = np.expand_dims(data, axis=0)
predict = model.predict(img)
# # 이미지 그리기
# plt.pcolormesh(predict)
# plt.clim()
# plt.colorbar()
# plt.show()
#
# breakpoint()
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
model.compile(loss=MeanSquaredError(),
optimizer=Adam(learning_rate=0.001),
# metrics=['accuracy'])
metrics=[PSNRLoss])
import random
import os
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import LearningRateScheduler
from tensorflow import math, int64, cast
def gen(features, labels, batch_size, patch_size):
# Create empty arrays to contain batch of features and labels#
batch_features = np.zeros((batch_size, patch_size[0], patch_size[1], 1))
batch_labels = np.zeros((batch_size, patch_size[0], patch_size[1], 1))
while True:
for i in range(batch_size):
def VDSR(in_ch, n_class, height, width):
inputs = Input(shape=(height, width, in_ch))
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(inputs)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(in_ch, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
res_img = model
outputs = add([res_img, inputs]) #[:,:,:,0:3]])
#model = Activation('softmax')(outputs)
outputs = Conv2D(n_class, (3, 3), activation='softmax',
padding='same')(outputs)
model = Model(inputs=[inputs], outputs=[outputs])
model.summary()
model.compile(optimizer=Adam(lr=1e-4),
loss='categorical_crossentropy',
metrics=[PSNR, 'accuracy'])
return model