fix code style

paddle_fl/examples/secagg_demo/fl_trainer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.core.master.fl_job import FLRunTimeJob
 import numpy
@@ -11,27 +25,32 @@ import math
 import hashlib
 import hmac
 
-logging.basicConfig(filename="log/test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="log/test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 logger = logging.getLogger("FLTrainer")
 
 BATCH_SIZE = 64
 
 train_reader = paddle.batch(
-    paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=500),
     batch_size=BATCH_SIZE)
-test_reader = paddle.batch(
-            paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
+test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 
 trainer_num = 2
-trainer_id = int(sys.argv[1]) # trainer id for each guest
+trainer_id = int(sys.argv[1])  # trainer id for each guest
 
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer.trainer_id = trainer_id
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.trainer_num = trainer_num
 trainer.key_dir = "./keys/"
 trainer.start()
@@ -47,8 +66,8 @@ feeder = fluid.DataFeeder(feed_list=[inputs, label], place=fluid.CPUPlace())
 # for test
 test_program = trainer._main_program.clone(for_test=True)
 
-def train_test(train_test_program,
-                   train_test_feed, train_test_reader):
+
+def train_test(train_test_program, train_test_feed, train_test_reader):
     acc_set = []
     avg_loss_set = []
     for test_data in train_test_reader():
@@ -61,6 +80,8 @@ def train_test(train_test_program,
     acc_val_mean = numpy.array(acc_set).mean()
     avg_loss_val_mean = numpy.array(avg_loss_set).mean()
     return avg_loss_val_mean, acc_val_mean
+
+
 # for test
 
 while not trainer.stop():
@@ -71,15 +92,18 @@ while not trainer.stop():
         step_i += 1
         trainer.step_id = step_i
         accuracy, = trainer.run(feed=feeder.feed(data),
-            fetch=["accuracy_0.tmp_0"])
+                                fetch=["accuracy_0.tmp_0"])
         if step_i % 100 == 0:
-            print("Epoch: {0}, step: {1}, accuracy: {2}".format(epoch_id, step_i, accuracy[0]))
+            print("Epoch: {0}, step: {1}, accuracy: {2}".format(
+                epoch_id, step_i, accuracy[0]))
 
     print(step_i)
-    avg_loss_val, acc_val = train_test(train_test_program=test_program,
-                                       train_test_reader=test_reader,
-                                       train_test_feed=feeder)
-    print("Test with Epoch %d, avg_cost: %s, acc: %s" %(epoch_id, avg_loss_val, acc_val))
+    avg_loss_val, acc_val = train_test(
+        train_test_program=test_program,
+        train_test_reader=test_reader,
+        train_test_feed=feeder)
+    print("Test with Epoch %d, avg_cost: %s, acc: %s" %
+          (epoch_id, avg_loss_val, acc_val))
 
     if epoch_id > 40:
         break

setup.py

@@ -26,10 +26,12 @@ from paddle_fl.version import fl_version
 def python_version():
     return [int(v) for v in platform.python_version().split(".")]
 
+
 max_version, mid_version, min_version = python_version()
 
 REQUIRED_PACKAGES = [
-    'six >= 1.10.0', 'protobuf >= 3.1.0','paddlepaddle >= 1.6', 'zmq', 'paddlepaddle-gpu >= 1.6' 
+    'six >= 1.10.0', 'protobuf >= 3.1.0', 'paddlepaddle >= 1.6', 'zmq',
+    'paddlepaddle-gpu >= 1.6'
 ]
 
 if max_version < 3:
@@ -42,8 +44,7 @@ REQUIRED_PACKAGES += ["unittest2"]
 setup(
     name='paddle_fl',
     version=fl_version.replace('-', ''),
-    description=
-    ('Federated Deep Learning Package Based on PaddlePaddle.'),
+    description=('Federated Deep Learning Package Based on PaddlePaddle.'),
     long_description='',
     url='https://github.com/PaddlePaddle/PaddleFL',
     author='PaddlePaddle Author',
@@ -70,4 +71,5 @@ setup(
         'Topic :: Software Development :: Libraries :: Python Modules',
     ],
     license='Apache 2.0',
-    keywords=('paddle_fl paddlepaddle multi-task transfer distributed-training'))
+    keywords=(
+        'paddle_fl paddlepaddle multi-task transfer distributed-training'))