def main():
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
time_list = []
for i in range(5):# 5 runs for average
args.id = str(i)# id
tf.reset_default_graph()
with tf.Session(config=config) as sess:
args.result = os.path.join(args.result, args.id)
args.log = os.path.join(args.log, args.id)
args.model = os.path.join(args.model, args.id)
args.tfrecords = os.path.join(args.tfrecords, args.id)
if not os.path.exists(args.model):
os.mkdir(args.model)
if not os.path.exists(args.log):
os.mkdir(args.log)
if not os.path.exists(args.result):
os.mkdir(args.result)
if not os.path.exists(args.tfrecords):
os.mkdir(args.tfrecords)
data_model = Data(args)
data_model.read_data()
dataset_train = data_model.data_parse(
os.path.join(args.tfrecords, 'train_data.tfrecords'), type='train')
dataset_test = data_model.data_parse(
os.path.join(args.tfrecords, 'test_data.tfrecords'), type='test')
dataset_sae_train = data_model.data_parse(
os.path.join(args.tfrecords, 'sae_train_data_' + str(args.sae_ratio) + '.tfrecords'), type='sae_train')
dataset_sae_test = data_model.data_parse(
os.path.join(args.tfrecords, 'sae_test_data.tfrecords'), type='sae_test')
dataset_de_map = data_model.data_parse(
os.path.join(args.tfrecords, 'demap_data' + '.tfrecords'), type='demap')
model = Model(args,sess)
if args.load_model:
model.load(args.model)
else:
model.train(dataset_train,dataset_sae_train,data_model)
a = datetime.now()
model.test(dataset_test)
b = datetime.now()
time_list.append((b-a).total_seconds())
if args.get_decode_map:
model.save_decode_map(dataset_de_map)
if args.get_decode_image:
model.get_decode_image(dataset_sae_test)
if args.get_feature:
model.get_feature(dataset_sae_test)
args.result = 'result'
args.log = 'log'
args.tfrecords = 'tfrecords'
args.model = 'model'
def main():
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
for i in range(1):
args.id = str(i)
tf.reset_default_graph()
with tf.Session(config=config) as sess:
args.result = os.path.join(args.result, args.id)
args.log = os.path.join(args.log, args.id)
args.model = os.path.join(args.model, args.id)
args.tfrecords = os.path.join(args.tfrecords, args.id)
if not os.path.exists(args.model):
os.mkdir(args.model)
if not os.path.exists(args.log):
os.mkdir(args.log)
if not os.path.exists(args.result):
os.mkdir(args.result)
if not os.path.exists(args.tfrecords):
os.mkdir(args.tfrecords)
dataset = Data(args)
dataset.read_data()
train_dataset = dataset.data_parse(os.path.join(
args.tfrecords, 'train_data.tfrecords'),
type='train')
test_dataset = dataset.data_parse(os.path.join(
args.tfrecords, 'test_data.tfrecords'),
type='test')
map_dataset = dataset.data_parse(os.path.join(
args.tfrecords, 'map_data.tfrecords'),
type='map')
model = Model(args, sess)
if not args.load_model:
model.train(train_dataset)
else:
model.load(args.model)
model.test(test_dataset)
if args.save_decode_map:
model.save_decode_map(map_dataset)
args.result = 'result'
args.log = 'log'
args.tfrecords = 'tfrecords'
args.model = 'model'
def main(num, run_num):
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
overall_oa = [] # used to store OA
overall_aa = [] # used to store AA
overall_kappa = [] # used to store Kappa
overall_matrix = [
] # used to Used to store the resulting obfuscation matrix
for j in range(num): # In this experiment, num is 20
print('第' + str(j + 1) + "次运行：")
for i in range(1):
args.id = str(i)
tf.reset_default_graph()
with tf.Session(config=config) as sess:
args.result = os.path.join(args.result, args.id)
args.log = os.path.join(args.log, args.id)
args.model = os.path.join(args.model, args.id)
args.tfrecords = os.path.join(args.tfrecords, args.id)
if not os.path.exists(args.model):
os.mkdir(args.model)
if not os.path.exists(args.log):
os.mkdir(args.log)
if not os.path.exists(args.result):
os.mkdir(args.result)
if not os.path.exists(args.tfrecords):
os.mkdir(args.tfrecords)
dataset = Data(args)
#test_pos_all is used to draw a diagram with no background
test_pos_all = dataset.read_data()
# producing training dataset
train_dataset = dataset.data_parse(os.path.join(
args.tfrecords, 'train_data.tfrecords'),
type='train')
# producing testing dataset, or producing during use.
test_dataset = dataset.data_parse(os.path.join(
args.tfrecords, 'test_data.tfrecords'),
type='test')
#all_dataset = dataset.data_parse(os.path.join(args.tfrecords, 'map_data.tfrecords'), type='map')
model = Model(args, sess)
# traing the model
if not args.load_model:
model.train(train_dataset, dataset)
else:
model.load(args.model)
# the model return the result of experiments once.
oa, aa, kappa, matrix, result_label = model.test(dataset)
# Calculated running time
over = time.time()
print("平均运行时间: " + str((over - start) / 60.0) + " min")
#create_All_label(model,args.data_name,dataset,j) # draw picture containing background
#decode_map(test_pos_all, result_label,args.data_name) # draw picture not containing background
overall_oa.append(oa)
overall_aa.append(aa)
overall_kappa.append(kappa)
overall_matrix.append(matrix)
args.result = 'result'
args.log = 'log'
args.tfrecords = 'tfrecords'
args.model = 'model'
# Store the results of the experiment locally
if not os.path.exists(str(pathlib.Path(args.data_name))):
os.mkdir(str(pathlib.Path(args.data_name)))
sio.savemat(
os.path.join(str(pathlib.Path(args.data_name)),
'result' + str(run_num) + '.mat'), {
'oa': overall_oa,
'aa': overall_aa,
'kappa': overall_kappa
})
sio.savemat(
os.path.join(str(pathlib.Path(args.data_name)),
'matrix' + str(run_num) + '.mat'),
{'matrix': overall_matrix})
def main():
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
time_list = []
for i in range(1):
args.id = str(i)# id
tf.reset_default_graph()
with tf.Session(config=config) as sess:
args.result = os.path.join(args.result, args.id)
args.log = os.path.join(args.log, args.id)
args.model = os.path.join(args.model, args.id)
args.tfrecords = os.path.join(args.tfrecords, args.id)
if not os.path.exists(args.model):
os.mkdir(args.model)
if not os.path.exists(args.log):
os.mkdir(args.log)
if not os.path.exists(args.result):
os.mkdir(args.result)
if not os.path.exists(args.tfrecords):
os.mkdir(args.tfrecords)
print("##################### Loading Data!! #####################")
data_model = Data(args)
data_model.read_data()
dataset_train = data_model.data_parse(
os.path.join(args.tfrecords, 'train_data.tfrecords'), type='train')
dataset_test = data_model.data_parse(
os.path.join(args.tfrecords, 'test_data.tfrecords'), type='test')
dataset_sae_train = data_model.data_parse(
os.path.join(args.tfrecords, 'sae_train_data_' + str(args.sae_ratio) + '.tfrecords'), type='sae_train')
dataset_sae_test = data_model.data_parse(
os.path.join(args.tfrecords, 'sae_test_data.tfrecords'), type='sae_test')
dataset_de_map = data_model.data_parse(
os.path.join(args.tfrecords, 'demap_data' + '.tfrecords'), type='demap')
print("##################### Data_Load Finished!! #####################")
#print("dataset_train:",dataset_train)
# dataset_train = tf.reshape(dataset_train, shape=[-1,220])
print("dataset_train:",type(dataset_train)) #(?, 1, 1, 220, 1)
model = Model(args,sess)
if args.load_model:
model.load(args.model)
else:
model.train(dataset_train,dataset_sae_train,data_model)
a = datetime.now()
model.test(dataset_test)
b = datetime.now()
time_list.append((b-a).total_seconds())
if args.get_decode_map:
model.save_decode_map(dataset_de_map)
#if args.get_decode_image:
# model.get_decode_image(dataset_sae_test)
#if args.get_feature:
# model.get_feature(dataset_sae_test)
args.result = 'result'
args.log = 'log'
args.tfrecords = 'tfrecords'
args.model = 'model'
print("Begin write time!....")
f = open(os.path.join(args.result,'time.txt'),'a')
f.write(str(time_list))
f.close()
sio.savemat(os.path.join(args.result,'time.mat'),{'time_list':time_list})
print("END!....")